{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/outputs/log_all.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}17 May 2025, 08:04:10
{txt}
{com}. 
. set seed 1
{txt}
{com}. 
{txt}end of do-file

{com}. do "/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/do_files/3_table_D1.do"
{txt}
{com}. * This is the do file to create "Table D1. Hausman-Wise Test"
. set seed 1
{txt}
{com}. 
. use "$path_data/temp/student_unbalance", clear
{txt}
{com}. 
. gen treatment_missing_dummy = 1 if treatment == .
{txt}(1,005 missing values generated)

{com}. recode treatment_missing_dummy (.=0)
{txt}(1,005 changes made to {bf:treatment_missing_dummy})

{com}. replace treatment = 0 if treatment == .
{txt}(50 real changes made)

{com}. 
. gen DT_score_pre_missing_0 = 0 if DT_score_pre == .
{txt}(968 missing values generated)

{com}. replace DT_score_pre_missing_0 = DT_score_pre if DT_score_pre != .
{txt}(968 real changes made)

{com}. 
. gen rosen_pre_missing_0 = 0 if rosen_pre == .
{txt}(1,011 missing values generated)

{com}. replace rosen_pre_missing_0 = rosen_pre if rosen_pre != .
{txt}(1,011 real changes made)

{com}. 
. gen cpcs_pre_missing_0 = 0 if cpcs_pre == .
{txt}(1,011 missing values generated)

{com}. replace cpcs_pre_missing_0 = cpcs_pre if cpcs_pre != .
{txt}(1,011 real changes made)

{com}. 
. gen DT_score_pre_missing_dummy = 1 if DT_score_pre == .
{txt}(968 missing values generated)

{com}. gen rosen_pre_missing_dummy = 1 if rosen_pre == .
{txt}(1,011 missing values generated)

{com}. gen cpcs_pre_missing_dummy = 1 if cpcs_pre == .
{txt}(1,011 missing values generated)

{com}. recode DT_score_pre_missing_dummy rosen_pre_missing_dummy cpcs_pre_missing_dummy (.=0)
{txt}(968 changes made to {bf:DT_score_pre_missing_dummy})
(1,011 changes made to {bf:rosen_pre_missing_dummy})
(1,011 changes made to {bf:cpcs_pre_missing_dummy})

{com}. 
. gen grade_missing_0 = 0 if grade == .
{txt}(1,005 missing values generated)

{com}. replace grade_missing_0 = grade if grade != .
{txt}(1,005 real changes made)

{com}. gen student_gender_missing_0 = 0 if student_gender == .
{txt}(978 missing values generated)

{com}. replace student_gender_missing_0 = grade if student_gender != .
{txt}(978 real changes made)

{com}. gen grade_missing_dummy = 1 if grade == .
{txt}(1,005 missing values generated)

{com}. gen student_gender_missing_dummy = 1 if student_gender == .
{txt}(978 missing values generated)

{com}. recode grade_missing_dummy student_gender_missing_dummy (.=0)
{txt}(1,005 changes made to {bf:grade_missing_dummy})
(978 changes made to {bf:student_gender_missing_dummy})

{com}. 
. *** full sample
. wildbootstrap reg attrition treatment treatment_missing_dummy, cluster(school_no) reps(1000)
{txt}{p 0 6 2}note: {bf:treatment_missing_dummy} omitted because of collinearity.{p_end}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{res}
{txt}Wild cluster bootstrap{col 52}{txt}Number of obs{col 70} = {res}1,005
{txt}Linear regression{col 52}{txt}Number of clusters{col 70} = {res}   34
{col 52}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 67}{txt}min{col 70} = {res}   16
{txt}Error weight: Rademacher{col 67}{txt}avg{col 70} = {res} 29.6
{col 67}{txt}max{col 70} = {res}   38
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}               attrition{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0701214{col 38}{space 1}  -1.75{col 46}{space 3}0.088{col 54}{space 3}-.1547603{col 66}{space 3} .0123998
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. mat hausman_simple = r(table)
{txt}
{com}. scalar n_hausman_simple = e(N)
{txt}
{com}. mean attrition if treatment == 0
{res}
{txt}{col 1}Mean estimation{col 44}{lalign 13:Number of obs}{col 57} = {res}{ralign 3:528}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}       Mean{col 26}   Std. err.{col 38}     [95% con{col 51}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 3}attrition {c |}{col 14}{res}{space 2} .8143939{col 26}{space 2} .0169359{col 37}{space 5} .7811238{col 51}{space 3} .8476641
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. matrix mean_hausman_simple = e(b)
{txt}
{com}. 
. wildbootstrap reg attrition treatment DT_score_pre_missing_0 rosen_pre_missing_0 cpcs_pre_missing_0 DT_score_pre_missing_dummy rosen_pre_missing_dummy cpcs_pre_missing_dummy i.grade_missing_0 student_gender_missing_0 grade_missing_dummy student_gender_missing_dummy treatment_missing_dummy, cluster(school_no) reps(1000)
{txt}{p 0 6 2}note: {bf:rosen_pre_missing_dummy} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:cpcs_pre_missing_dummy} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:grade_missing_dummy} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:treatment_missing_dummy} omitted because of collinearity.{p_end}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:DT_score_pre_missing_0 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:DT_score_pre_missing_0}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:rosen_pre_missing_0 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:rosen_pre_missing_0}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}30{text} done{text} ({result:30})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:cpcs_pre_missing_0 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:cpcs_pre_missing_0}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:DT_score_pre_missing_dummy = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:DT_score_pre_missing_dummy}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:4.grade_missing_0 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:4.grade_missing_0}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:student_gender_missing_0 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:student_gender_missing_0}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:student_gender_missing_dummy = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:student_gender_missing_dummy}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 52}{txt}Number of obs{col 70} = {res}1,005
{txt}Linear regression{col 52}{txt}Number of clusters{col 70} = {res}   34
{col 52}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 67}{txt}min{col 70} = {res}   16
{txt}Error weight: Rademacher{col 67}{txt}avg{col 70} = {res} 29.6
{col 67}{txt}max{col 70} = {res}   38
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}               attrition{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraints             {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0701666{col 38}{space 1}  -1.89{col 46}{space 3}0.094{col 54}{space 3}-.1480083{col 66}{space 3} .0093907
{col 1}{text} DT_score_pre_missing_0 {col 26}{c |}
{res}{col 1}{text}                     = 0{col 26}{c |}{result}{space 2}-.0424797{col 38}{space 1}  -3.00{col 46}{space 3}0.006{col 54}{space 3}-.0714787{col 66}{space 3}-.0123517
{col 26}{text}{c |}
{res}{col 1}{text} rosen_pre_missing_0 = 0{col 26}{c |}{result}{space 2}-.0106981{col 38}{space 1}  -0.79{col 46}{space 3}0.470{col 54}{space 3}-.0371246{col 66}{space 3} .0176461
{col 1}{text}  cpcs_pre_missing_0 = 0{col 26}{c |}{result}{space 2} .0126426{col 38}{space 1}   1.22{col 46}{space 3}0.266{col 54}{space 3}-.0086699{col 66}{space 3} .0349642
{col 1}{text}DT_score_pre_missing_dum{col 26}{c |}
{res}{col 1}{text}                  my = 0{col 26}{c |}{result}{space 2} .0234753{col 38}{space 1}   0.30{col 46}{space 3}0.778{col 54}{space 3}-.2754783{col 66}{space 3}  .273463
{col 26}{text}{c |}
{res}{col 1}{text}   4.grade_missing_0 = 0{col 26}{c |}{result}{space 2} .1238316{col 38}{space 1}   1.40{col 46}{space 3}0.116{col 54}{space 3}-.0388849{col 66}{space 3} .4047513
{col 1}{text}student_gender_missing_0{col 26}{c |}
{res}{col 1}{text}                     = 0{col 26}{c |}{result}{space 2}-.0370784{col 38}{space 1}  -0.78{col 46}{space 3}0.414{col 54}{space 3}-.1815315{col 66}{space 3} .0641346
{col 26}{text}{c |}
{res}{col 1}{text}student_gender_missing_d{col 26}{c |}
{res}{col 1}{text}                ummy = 0{col 26}{c |}{result}{space 2}   .04188{col 38}{space 1}   0.21{col 46}{space 3}0.846{col 54}{space 3}-.6908404{col 66}{space 3} .4037143
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. mat hausman_control = r(table)
{txt}
{com}. scalar n_hausman_control = e(N)
{txt}
{com}. 
. *** subsample
. egen DT_score_pre_med = median(DT_score_pre)
{txt}
{com}. gen DT_score_pre_upper50 = 1 if DT_score_pre>DT_score_pre_med
{txt}(491 missing values generated)

{com}. recode DT_score_pre_upper50 (.=0)
{txt}(491 changes made to {bf:DT_score_pre_upper50})

{com}. 
. wildbootstrap reg attrition treatment DT_score_pre rosen_pre_missing_0 cpcs_pre_missing_0 rosen_pre_missing_dummy cpcs_pre_missing_dummy i.grade_missing_0 student_gender_missing_0 grade_missing_dummy student_gender_missing_dummy treatment_missing_dummy if DT_score_pre_upper50 == 1, cluster(school_no) reps(1000)
{txt}{p 0 6 2}note: {bf:rosen_pre_missing_dummy} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:cpcs_pre_missing_dummy} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:grade_missing_dummy} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:treatment_missing_dummy} omitted because of collinearity.{p_end}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:DT_score_pre = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:DT_score_pre}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:rosen_pre_missing_0 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:rosen_pre_missing_0}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:cpcs_pre_missing_0 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:cpcs_pre_missing_0}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:4.grade_missing_0 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:4.grade_missing_0}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}30{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}40{text}.{text}.{text} done{text} ({result:42})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}30{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:39})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:student_gender_missing_0 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:student_gender_missing_0}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}30{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}40{text}.{text} done{text} ({result:41})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}30{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:37})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:student_gender_missing_dummy = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:student_gender_missing_dummy}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}30{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:39})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}30{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:38})
{res}
{txt}Wild cluster bootstrap{col 53}{txt}Number of obs{col 71} = {res} 477
{txt}Linear regression{col 53}{txt}Number of clusters{col 71} = {res}  34
{col 53}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 68}{txt}min{col 71} = {res}   2
{txt}Error weight: Rademacher{col 68}{txt}avg{col 71} = {res}14.0
{col 68}{txt}max{col 71} = {res}  29
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}               attrition{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraints             {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0647459{col 38}{space 1}  -1.44{col 46}{space 3}0.200{col 54}{space 3}-.1555153{col 66}{space 3} .0342288
{col 1}{text}        DT_score_pre = 0{col 26}{c |}{result}{space 2}-.1086732{col 38}{space 1}  -1.80{col 46}{space 3}0.090{col 54}{space 3}-.2469317{col 66}{space 3} .0241114
{col 1}{text} rosen_pre_missing_0 = 0{col 26}{c |}{result}{space 2} .0049081{col 38}{space 1}   0.22{col 46}{space 3}0.826{col 54}{space 3}-.0399779{col 66}{space 3}  .051329
{col 1}{text}  cpcs_pre_missing_0 = 0{col 26}{c |}{result}{space 2} .0024088{col 38}{space 1}   0.12{col 46}{space 3}0.904{col 54}{space 3}-.0399247{col 66}{space 3} .0467619
{col 1}{text}   4.grade_missing_0 = 0{col 26}{c |}{result}{space 2}-.0021494{col 38}{space 1}  -0.05{col 46}{space 3}0.984{col 54}{space 3}-22.54113{col 66}{space 3} 14.17057
{col 1}{text}student_gender_missing_0{col 26}{c |}
{res}{col 1}{text}                     = 0{col 26}{c |}{result}{space 2} .0101931{col 38}{space 1}   0.34{col 46}{space 3}0.766{col 54}{space 3}-6.849473{col 66}{space 3}  8.68434
{col 26}{text}{c |}
{res}{col 1}{text}student_gender_missing_d{col 26}{c |}
{res}{col 1}{text}                ummy = 0{col 26}{c |}{result}{space 2} .3697013{col 38}{space 1}   4.31{col 46}{space 3}0.208{col 54}{space 3}-46.29288{col 66}{space 3} 35.28915
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. mat hausman_upper = r(table)
{txt}
{com}. scalar n_hausman_upper = e(N)
{txt}
{com}. mean attrition if treatment == 0 & DT_score_pre_upper50 == 1
{res}
{txt}{col 1}Mean estimation{col 44}{lalign 13:Number of obs}{col 57} = {res}{ralign 3:305}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}       Mean{col 26}   Std. err.{col 38}     [95% con{col 51}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 3}attrition {c |}{col 14}{res}{space 2} .8065574{col 26}{space 2} .0226546{col 37}{space 5} .7619776{col 51}{space 3} .8511371
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. matrix mean_hausman_upper = e(b)
{txt}
{com}. 
. 
. wildbootstrap reg attrition treatment DT_score_pre rosen_pre_missing_0 cpcs_pre_missing_0 rosen_pre_missing_dummy cpcs_pre_missing_dummy i.grade_missing_0 student_gender_missing_0 grade_missing_dummy student_gender_missing_dummy treatment_missing_dummy if DT_score_pre_upper50 == 0, cluster(school_no) reps(1000)
{txt}{p 0 6 2}note: {bf:rosen_pre_missing_dummy} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:cpcs_pre_missing_dummy} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:student_gender_missing_0} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:grade_missing_dummy} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:student_gender_missing_dummy} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:treatment_missing_dummy} omitted because of collinearity.{p_end}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:DT_score_pre = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:DT_score_pre}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:rosen_pre_missing_0 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:rosen_pre_missing_0}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:cpcs_pre_missing_0 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:cpcs_pre_missing_0}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}30{text} done{text} ({result:30})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:4.grade_missing_0 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:4.grade_missing_0}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 53}{txt}Number of obs{col 71} = {res} 491
{txt}Linear regression{col 53}{txt}Number of clusters{col 71} = {res}  34
{col 53}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 68}{txt}min{col 71} = {res}   3
{txt}Error weight: Rademacher{col 68}{txt}avg{col 71} = {res}14.4
{col 68}{txt}max{col 71} = {res}  28
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}               attrition{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraints             {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} -.085646{col 38}{space 1}  -1.71{col 46}{space 3}0.102{col 54}{space 3}-.1897519{col 66}{space 3}  .018472
{col 1}{text}        DT_score_pre = 0{col 26}{c |}{result}{space 2}-.0255488{col 38}{space 1}  -1.28{col 46}{space 3}0.192{col 54}{space 3}-.0710009{col 66}{space 3} .0159113
{col 1}{text} rosen_pre_missing_0 = 0{col 26}{c |}{result}{space 2}-.0252415{col 38}{space 1}  -1.77{col 46}{space 3}0.096{col 54}{space 3}-.0572147{col 66}{space 3} .0043096
{col 1}{text}  cpcs_pre_missing_0 = 0{col 26}{c |}{result}{space 2} .0230144{col 38}{space 1}   1.90{col 46}{space 3}0.066{col 54}{space 3}-.0013373{col 66}{space 3}  .051174
{col 1}{text}   4.grade_missing_0 = 0{col 26}{c |}{result}{space 2} .0837875{col 38}{space 1}   1.81{col 46}{space 3}0.102{col 54}{space 3}-.0202092{col 66}{space 3} .1821456
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. mat hausman_lower = r(table)
{txt}
{com}. scalar n_hausman_lower = e(N)
{txt}
{com}. mean attrition if treatment == 0 & DT_score_pre_upper50 == 0
{res}
{txt}{col 1}Mean estimation{col 44}{lalign 13:Number of obs}{col 57} = {res}{ralign 3:223}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}       Mean{col 26}   Std. err.{col 38}     [95% con{col 51}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 3}attrition {c |}{col 14}{res}{space 2} .8251121{col 26}{space 2} .0254953{col 37}{space 5} .7748684{col 51}{space 3} .8753559
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. matrix mean_hausman_lower = e(b)
{txt}
{com}. 
. wildbootstrap reg attrition treatment DT_score_pre_missing_0 rosen_pre_missing_0 cpcs_pre_missing_0 DT_score_pre_missing_dummy rosen_pre_missing_dummy cpcs_pre_missing_dummy student_gender_missing_0 student_gender_missing_dummy treatment_missing_dummy if grade == 4, cluster(school_no) reps(1000)
{txt}{p 0 6 2}note: {bf:rosen_pre_missing_dummy} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:cpcs_pre_missing_dummy} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:student_gender_missing_dummy} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:treatment_missing_dummy} omitted because of collinearity.{p_end}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:DT_score_pre_missing_0 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:DT_score_pre_missing_0}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:rosen_pre_missing_0 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:rosen_pre_missing_0}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:cpcs_pre_missing_0 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:cpcs_pre_missing_0}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:DT_score_pre_missing_dummy = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:DT_score_pre_missing_dummy}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}30{text}.{text} done{text} ({result:31})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:student_gender_missing_0 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:student_gender_missing_0}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{res}
{txt}Wild cluster bootstrap{col 53}{txt}Number of obs{col 71} = {res} 422
{txt}Linear regression{col 53}{txt}Number of clusters{col 71} = {res}  15
{col 53}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 68}{txt}min{col 71} = {res}  16
{txt}Error weight: Rademacher{col 68}{txt}avg{col 71} = {res}28.1
{col 68}{txt}max{col 71} = {res}  34
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}               attrition{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraints             {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.1216409{col 38}{space 1}  -2.57{col 46}{space 3}0.038{col 54}{space 3}-.2223043{col 66}{space 3}-.0074862
{col 1}{text} DT_score_pre_missing_0 {col 26}{c |}
{res}{col 1}{text}                     = 0{col 26}{c |}{result}{space 2}-.0395291{col 38}{space 1}  -2.48{col 46}{space 3}0.026{col 54}{space 3}-.0777602{col 66}{space 3}-.0053869
{col 26}{text}{c |}
{res}{col 1}{text} rosen_pre_missing_0 = 0{col 26}{c |}{result}{space 2}-.0221403{col 38}{space 1}  -1.34{col 46}{space 3}0.190{col 54}{space 3}-.0594264{col 66}{space 3} .0107674
{col 1}{text}  cpcs_pre_missing_0 = 0{col 26}{c |}{result}{space 2} .0147711{col 38}{space 1}   1.31{col 46}{space 3}0.230{col 54}{space 3}-.0113582{col 66}{space 3} .0385832
{col 1}{text}DT_score_pre_missing_dum{col 26}{c |}
{res}{col 1}{text}                  my = 0{col 26}{c |}{result}{space 2}-.0608206{col 38}{space 1}  -0.71{col 46}{space 3}0.468{col 54}{space 3}-.7009773{col 66}{space 3} .2611225
{col 26}{text}{c |}
{res}{col 1}{text}student_gender_missing_0{col 26}{c |}
{res}{col 1}{text}                     = 0{col 26}{c |}{result}{space 2}-.0642315{col 38}{space 1}  -3.10{col 46}{space 3}0.020{col 54}{space 3}-.1159247{col 66}{space 3}-.0277444
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. mat hausman_grade4 = r(table)
{txt}
{com}. scalar n_hausman_grade4 = e(N)
{txt}
{com}. mean attrition if treatment == 0 & grade == 4
{res}
{txt}{col 1}Mean estimation{col 44}{lalign 13:Number of obs}{col 57} = {res}{ralign 3:210}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}       Mean{col 26}   Std. err.{col 38}     [95% con{col 51}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 3}attrition {c |}{col 14}{res}{space 2} .8428571{col 26}{space 2} .0251739{col 37}{space 5} .7932298{col 51}{space 3} .8924845
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. matrix mean_hausman_grade4 = e(b)
{txt}
{com}. 
. 
. wildbootstrap reg attrition treatment DT_score_pre_missing_0 rosen_pre_missing_0 cpcs_pre_missing_0 DT_score_pre_missing_dummy rosen_pre_missing_dummy cpcs_pre_missing_dummy student_gender_missing_0 student_gender_missing_dummy treatment_missing_dummy if grade == 2, cluster(school_no) reps(1000)
{txt}{p 0 6 2}note: {bf:rosen_pre_missing_dummy} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:cpcs_pre_missing_dummy} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:student_gender_missing_dummy} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:treatment_missing_dummy} omitted because of collinearity.{p_end}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:DT_score_pre_missing_0 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:DT_score_pre_missing_0}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:19})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}30{text} done{text} ({result:30})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:rosen_pre_missing_0 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:rosen_pre_missing_0}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:cpcs_pre_missing_0 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:cpcs_pre_missing_0}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:DT_score_pre_missing_dummy = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:DT_score_pre_missing_dummy}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}30{text}.{text}.{text} done{text} ({result:32})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}30{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:35})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:student_gender_missing_0 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:student_gender_missing_0}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:29})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}30{text} done{text} ({result:30})
{res}
{txt}Wild cluster bootstrap{col 53}{txt}Number of obs{col 71} = {res} 583
{txt}Linear regression{col 53}{txt}Number of clusters{col 71} = {res}  19
{col 53}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 68}{txt}min{col 71} = {res}  27
{txt}Error weight: Rademacher{col 68}{txt}avg{col 71} = {res}30.7
{col 68}{txt}max{col 71} = {res}  38
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}               attrition{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraints             {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0325048{col 38}{space 1}  -0.65{col 46}{space 3}0.526{col 54}{space 3} -.131762{col 66}{space 3} .0717168
{col 1}{text} DT_score_pre_missing_0 {col 26}{c |}
{res}{col 1}{text}                     = 0{col 26}{c |}{result}{space 2}-.0470574{col 38}{space 1}  -2.25{col 46}{space 3}0.062{col 54}{space 3} -.093604{col 66}{space 3} .0024958
{col 26}{text}{c |}
{res}{col 1}{text} rosen_pre_missing_0 = 0{col 26}{c |}{result}{space 2}  .001101{col 38}{space 1}   0.06{col 46}{space 3}0.908{col 54}{space 3}-.0400439{col 66}{space 3} .0465926
{col 1}{text}  cpcs_pre_missing_0 = 0{col 26}{c |}{result}{space 2} .0103347{col 38}{space 1}   0.61{col 46}{space 3}0.488{col 54}{space 3}-.0242069{col 66}{space 3} .0442243
{col 1}{text}DT_score_pre_missing_dum{col 26}{c |}
{res}{col 1}{text}                  my = 0{col 26}{c |}{result}{space 2} .1514967{col 38}{space 1}   1.41{col 46}{space 3}0.404{col 54}{space 3}-5.386859{col 66}{space 3} 15.01056
{col 26}{text}{c |}
{res}{col 1}{text}student_gender_missing_0{col 26}{c |}
{res}{col 1}{text}                     = 0{col 26}{c |}{result}{space 2}-.0063052{col 38}{space 1}  -0.09{col 46}{space 3}0.960{col 54}{space 3}-1.751272{col 66}{space 3} .6685841
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. mat hausman_grade3 = r(table)
{txt}
{com}. scalar n_hausman_grade3 = e(N)
{txt}
{com}. mean attrition if treatment == 0 & grade == 2
{res}
{txt}{col 1}Mean estimation{col 44}{lalign 13:Number of obs}{col 57} = {res}{ralign 3:268}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}       Mean{col 26}   Std. err.{col 38}     [95% con{col 51}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 3}attrition {c |}{col 14}{res}{space 2} .7574627{col 26}{space 2}  .026231{col 37}{space 5} .7058168{col 51}{space 3} .8091085
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. matrix mean_hausman_grade3 = e(b)
{txt}
{com}. 
. 
. local specification simple control upper lower grade4 grade3
{txt}
{com}. foreach spec in `specification'{c -(}
{txt}  2{com}.                 if hausman_`spec'[3,1]<=0.01 {c -(}
{txt}  3{com}.                         local star_`spec' %3s "***"
{txt}  4{com}.                 {c )-}
{txt}  5{com}.                 else if (hausman_`spec'[3,1]>0.01) & (hausman_`spec'[3,1]<=0.05) {c -(}
{txt}  6{com}.                         local star_`spec' %2s "**"
{txt}  7{com}.                 {c )-}
{txt}  8{com}.                 else if (hausman_`spec'[3,1]>0.05) & (hausman_`spec'[3,1]<=0.10) {c -(}
{txt}  9{com}.                         local star_`spec' %1s "*"
{txt} 10{com}.                 {c )-}
{txt} 11{com}.                 else {c -(}
{txt} 12{com}.                         local star_`spec'  ""
{txt} 13{com}.                 {c )-}
{txt} 14{com}. {c )-}
{txt}
{com}. 
. /// Table
> 
. tempname hh2
{txt}
{com}. file open `hh2' using "$path_output/hausman_test.tex", write replace
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "% Author: Kazuma Takakura" _newline
{txt}
{com}. file write `hh2' "% Date: `c(current_date)'" _newline
{txt}
{com}. file write `hh2' "% Time: `c(current_time)'" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. 
. file write `hh2' "\begin{c -(}table{c )-}[h!]\footnotesize" _newline
{txt}
{com}. file write `hh2' "  \centering" _newline
{txt}
{com}. file write `hh2' "  \caption{c -(}Hausman-Wise Test{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:hausman{c )-}" _newline
{txt}
{com}. file write `hh2' "\scalebox{c -(}0.7{c )-}{c -(}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}threeparttable{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "\begin{c -(}tabular{c )-}{c -(}lcccccc{c )-}\toprule" _newline
{txt}
{com}. 
. 
. file write `hh2' "  & Full sample & Full sample & DT Score $>$ median & DT Score $\leq$ median & Grade 4 & Grade 3 \\" _newline
{txt}
{com}. 
. file write `hh2' "  Treatment & " %04.3f (hausman_simple[1,1]) `star_simple' "  & " %04.3f (hausman_control[1,1]) `star_control' " & " %04.3f (hausman_upper[1,1]) `star_upper' " & " %04.3f (hausman_lower[1,1]) `star_lower' " & " %04.3f (hausman_grade4[1,1]) `star_grade4' " & " %04.3f (hausman_grade3[1,1]) `star_grade3' " \\ " _newline
{txt}
{com}. file write `hh2' "    & (" %04.3f (hausman_simple[3,1]) ") & (" %04.3f (hausman_control[3,1]) ") & (" %04.3f (hausman_upper[3,1]) ") & (" %04.3f (hausman_lower[3,1]) ") & (" %04.3f (hausman_grade4[3,1]) ") & (" %04.3f (hausman_grade3[3,1]) ") \\ " _newline
{txt}
{com}. file write `hh2' "  Control Mean & " %04.3f (mean_hausman_simple[1,1]) "  & " %04.3f (mean_hausman_simple[1,1]) " & " %04.3f (mean_hausman_upper[1,1]) " & " %04.3f (mean_hausman_lower[1,1]) " & " %04.3f (mean_hausman_grade4[1,1]) " & " %04.3f (mean_hausman_grade3[1,1])  " \\ " _newline
{txt}
{com}. 
. file write `hh2' "  Control & N & Y & Y & Y & Y & Y \\ " _newline
{txt}
{com}. file write `hh2' "  Observations & " (n_hausman_simple) "  & " (n_hausman_control) " & " (n_hausman_upper) " & " (n_hausman_lower) "  & " (n_hausman_grade4) " & " (n_hausman_grade3) " \\ " _newline
{txt}
{com}. 
. 
. file write `hh2' "\midrule" _newline
{txt}
{com}. file write `hh2' "\end{c -(}tabular{c )-}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\item (a) Dependent variable is the attrition dummy." _newline
{txt}
{com}. file write `hh2' "\item (b) Controls are the students' grade, sex, baseline cognitive and baseline non-cognitive scores." _newline
{txt}
{com}. file write `hh2' "\item (c) Wild clustered bootstrap p-values are reported within parentheses. Clusters are schools at the baseline. There are 34 clusters. " _newline
{txt}
{com}. file write `hh2' "\item (d) $^*$ Significant at 10\% level; $^{c -(}**{c )-}$ significant at 5\% level; $^{c -(}***{c )-}$ significant at 1\% level. " _newline
{txt}
{com}. file write `hh2' "\end{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}threeparttable{c )-}" _newline
{txt}
{com}. file write `hh2' "{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}table{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. file close `hh2'
{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/mb/s9qljd594gbfyhl2dqnnwlcw0000gn/T//SD56186.000000"
{txt}
{com}. version 18.5
{txt}
{com}. clear all
{res}{txt}
{com}. set more off
{txt}
{com}. 
. 
. * set the path to global
. 
. global path_replication "CHANGE TO YOUR PATH"
{txt}
{com}. 
. global path_output "$path_replication/outputs"
{txt}
{com}. 
. global path_data "$path_replication/data"
{txt}
{com}. 
. global path_do "$path_replication/do_files"
{txt}
{com}. 
. adopath + "$path_replication/ado"
{txt}  [1]  (BASE)      "{res}/Applications/Stata/ado/base/{txt}"
  [2]  (SITE)      "{res}/Applications/Stata/ado/site/{txt}"
  [3]              "{res}.{txt}"
  [4]  (PERSONAL)  "{res}/Users/takakurakazuma/Documents/Stata/ado/personal/{txt}"
  [5]  (PLUS)      "{res}/Users/takakurakazuma/Library/Application Support/Stata/ado/plus/{txt}"
  [6]  (OLDPLACE)  "{res}~/ado/{txt}"
  [7]              "{res}/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/ado{txt}"
  [8]              "{res}CHANGE TO YOUR PATH/ado{txt}"

{com}. 
. 
. 
. log using "$path_output/log_all", replace
{err}log file already open
{txt}{search r(604), local:r(604);}

end of do-file

{search r(604), local:r(604);}

{com}. do "/var/folders/mb/s9qljd594gbfyhl2dqnnwlcw0000gn/T//SD56186.000000"
{txt}
{com}. version 18.5
{txt}
{com}. clear all
{res}{txt}
{com}. set more off
{txt}
{com}. 
. 
. * set the path to global
. 
. global path_replication "/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package"
{txt}
{com}. 
. global path_output "$path_replication/outputs"
{txt}
{com}. 
. global path_data "$path_replication/data"
{txt}
{com}. 
. global path_do "$path_replication/do_files"
{txt}
{com}. 
. adopath + "$path_replication/ado"
{txt}  [1]  (BASE)      "{res}/Applications/Stata/ado/base/{txt}"
  [2]  (SITE)      "{res}/Applications/Stata/ado/site/{txt}"
  [3]              "{res}.{txt}"
  [4]  (PERSONAL)  "{res}/Users/takakurakazuma/Documents/Stata/ado/personal/{txt}"
  [5]  (PLUS)      "{res}/Users/takakurakazuma/Library/Application Support/Stata/ado/plus/{txt}"
  [6]  (OLDPLACE)  "{res}~/ado/{txt}"
  [7]              "{res}CHANGE TO YOUR PATH/ado{txt}"
  [8]              "{res}/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/ado{txt}"

{com}. 
. 
. 
. * log using "$path_output/log_all", replace
. 
. set seed 1234
{txt}
{com}. 
. **************************************************
. 
. 
. *** run the code for cleaning.
. 
. do "$path_do/1_data_cleaning_students.do"
{txt}
{com}. clear all
{res}{txt}
{com}. set more off
{txt}
{com}. 
. 
. /// prepare baseline teacher information
> use "$path_data/original_teacher.dta", clear
{txt}
{com}. drop if endline == 1
{txt}(1,004 observations deleted)

{com}. keep student_no age_tchr gender_tchr edu_tchr
{txt}
{com}. sort student_no
{txt}
{com}. save "$path_data/temp/teacher", replace
{txt}{p 0 4 2}
file {bf}
/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/data/temp/teacher.dta{rm}
saved
{p_end}

{com}. 
. import excel "$path_data/followup_students_master.xlsx", clear first
{res}{text}(255 vars, 287 obs)

{com}. gen student_no = q1b
{txt}
{com}. 
. /// remove imcomplete interview
> drop if q0a == "319003"
{txt}(1 observation deleted)

{com}. 
. /// Yes==1, No==0, Dont know ==.
> recode q2a q2c q2h q3a q3e q4a q7a q7b q9a1 q9a3 q9b1 (2=0)
{txt}(127 changes made to {bf:q2a})
(268 changes made to {bf:q2c})
(173 changes made to {bf:q2h})
(170 changes made to {bf:q3a})
(41 changes made to {bf:q3e})
(24 changes made to {bf:q4a})
(56 changes made to {bf:q7a})
(11 changes made to {bf:q7b})
(24 changes made to {bf:q9a1})
(267 changes made to {bf:q9a3})
(0 changes made to {bf:q9b1})

{com}. recode q9b1 (3=.)
{txt}(2 changes made to {bf:q9b1})

{com}. 
. 
. 
. 
. // other changes
. 
. gen PSC_grade = q2k2
{txt}(62 missing values generated)

{com}. replace PSC_grade ="0" if q2k2== "Auto"
{txt}(5 real changes made)

{com}. replace PSC_grade ="0" if q2k2== "mone nai"
{txt}(1 real change made)

{com}. replace PSC_grade ="0" if q2k2== ""
{txt}(62 real changes made)

{com}. replace PSC_grade ="3.08" if q2k2=="3.o8"
{txt}(1 real change made)

{com}. destring PSC_grade, replace
{txt}PSC_grade: all characters numeric; {res}replaced {txt}as {res}double
{txt}
{com}. recode PSC_grade(0=.)
{txt}(68 changes made to {bf:PSC_grade})

{com}. 
. gen JSC_grade = q2l2
{txt}(146 missing values generated)

{com}. gen JSC_auto = 0
{txt}
{com}. replace JSC_auto = 1 if q2l2 == "Ato pas"
{txt}(3 real changes made)

{com}. replace JSC_auto = 1 if q2l2 == "Atou pass"
{txt}(1 real change made)

{com}. replace JSC_auto = 1 if q2l2 == "Auto"
{txt}(32 real changes made)

{com}. replace JSC_auto = 1 if q2l2 == "Auto  pass"
{txt}(4 real changes made)

{com}. replace JSC_auto = 1 if q2l2 == "Auto Pass"
{txt}(8 real changes made)

{com}. replace JSC_auto = 1 if q2l2 == "Auto pass"
{txt}(22 real changes made)

{com}. replace JSC_auto = 1 if q2l2 == "Autopash."
{txt}(1 real change made)

{com}. replace JSC_auto = 1 if q2l2 == "Autopass"
{txt}(1 real change made)

{com}. replace JSC_auto = 1 if q2l2 == "auto pass"
{txt}(11 real changes made)

{com}. replace JSC_auto = 1 if q2l2 == "result school thake deyni"
{txt}(1 real change made)

{com}. replace JSC_grade = "0" if JSC_auto == 1
{txt}(84 real changes made)

{com}. replace JSC_grade = "0" if q2l2 == ""
{txt}(146 real changes made)

{com}. destring JSC_grade, replace
{txt}JSC_grade: all characters numeric; {res}replaced {txt}as {res}double
{txt}
{com}. recode JSC_grade(0=.)
{txt}(230 changes made to {bf:JSC_grade})

{com}. 
. 
. local q5an 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
{txt}
{com}. foreach i in `q5an'{c -(}
{txt}  2{com}. gen q5a_`i' = 1 if q5a==`i'
{txt}  3{com}. recode q5a_`i'(.=0)
{txt}  4{com}. {c )-}
{txt}(285 missing values generated)
(285 changes made to {bf:q5a_1})
(270 missing values generated)
(270 changes made to {bf:q5a_2})
(265 missing values generated)
(265 changes made to {bf:q5a_3})
(188 missing values generated)
(188 changes made to {bf:q5a_4})
(264 missing values generated)
(264 changes made to {bf:q5a_5})
(284 missing values generated)
(284 changes made to {bf:q5a_6})
(282 missing values generated)
(282 changes made to {bf:q5a_7})
(263 missing values generated)
(263 changes made to {bf:q5a_8})
(285 missing values generated)
(285 changes made to {bf:q5a_9})
(286 missing values generated)
(286 changes made to {bf:q5a_10})
(286 missing values generated)
(286 changes made to {bf:q5a_11})
(217 missing values generated)
(217 changes made to {bf:q5a_12})
(279 missing values generated)
(279 changes made to {bf:q5a_13})
(286 missing values generated)
(286 changes made to {bf:q5a_14})
(284 missing values generated)
(284 changes made to {bf:q5a_15})
(286 missing values generated)
(286 changes made to {bf:q5a_16})

{com}. 
. local q5bn 1 2 3 4 5 6 7 8 9
{txt}
{com}. foreach i in `q5bn'{c -(}
{txt}  2{com}. gen q5b_`i' = 1 if q5b==`i'
{txt}  3{com}. recode q5b_`i'(.=0)
{txt}  4{com}. {c )-}
{txt}(243 missing values generated)
(243 changes made to {bf:q5b_1})
(189 missing values generated)
(189 changes made to {bf:q5b_2})
(260 missing values generated)
(260 changes made to {bf:q5b_3})
(281 missing values generated)
(281 changes made to {bf:q5b_4})
(231 missing values generated)
(231 changes made to {bf:q5b_5})
(285 missing values generated)
(285 changes made to {bf:q5b_6})
(284 missing values generated)
(284 changes made to {bf:q5b_7})
(277 missing values generated)
(277 changes made to {bf:q5b_8})
(283 missing values generated)
(283 changes made to {bf:q5b_9})

{com}. 
. 
. 
. gen q6a1_correct = 1 if q6a1==10800
{txt}(88 missing values generated)

{com}. gen q6a2_correct = 1 if q6a2==9
{txt}(43 missing values generated)

{com}. gen q6a3a_correct = 1 if q6a3a==70
{txt}(70 missing values generated)

{com}. gen q6a3b_correct = 1 if q6a3b==50
{txt}(46 missing values generated)

{com}. gen q6a4_correct = 1 if q6a4==20
{txt}(211 missing values generated)

{com}. gen q6a5_correct = 1 if q6a5==5
{txt}(224 missing values generated)

{com}. 
. recode q6a1_correct q6a2_correct q6a3a_correct q6a3b_correct q6a4_correct q6a5_correct (.=0)
{txt}(88 changes made to {bf:q6a1_correct})
(43 changes made to {bf:q6a2_correct})
(70 changes made to {bf:q6a3a_correct})
(46 changes made to {bf:q6a3b_correct})
(211 changes made to {bf:q6a4_correct})
(224 changes made to {bf:q6a5_correct})

{com}. 
. save "$path_data/temp/followup_student_data", replace
{txt}{p 0 4 2}
file {bf}
/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/data/temp/followup_student_data.dta{rm}
saved
{p_end}

{com}.  
. 
. import excel "$path_data/followup_students_extra.xlsx",  clear first
{res}{text}(50 vars, 222 obs)

{com}. drop if q1b==1223 & _index==64
{txt}(1 observation deleted)

{com}. drop if q1b==2804 & _index==116
{txt}(1 observation deleted)

{com}. keep q1b q3c1new q3c2new q3e _index
{txt}
{com}. rename q3e q3enew
{res}{txt}
{com}. recode q3enew(2=0)
{txt}(155 changes made to {bf:q3enew})

{com}. destring q1b, replace
{txt}q1b already numeric; no {res}replace
{txt}
{com}. merge 1:1 q1b using "$path_data/temp/followup_student_data"
{res}
{txt}{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}              68
{txt}{col 9}from master{col 30}{res}               1{txt}  (_merge==1)
{col 9}from using{col 30}{res}              67{txt}  (_merge==2)

{col 5}Matched{col 30}{res}             219{txt}  (_merge==3)
{col 5}{hline 41}

{com}. save "$path_data/temp/followup_student_data", replace
{txt}{p 0 4 2}
file {bf}
/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/data/temp/followup_student_data.dta{rm}
saved
{p_end}

{com}. 
. 
. // check the accuracy of q3c
. drop if _merge==1
{txt}(1 observation deleted)

{com}. drop _merge
{txt}
{com}. sum q3c1new

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}q3c1new {c |}{res}         94    5.851064    .6038843          4          7
{txt}
{com}. sum q3c2new

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}q3c2new {c |}{res}         94    9.829787    3.999028          4         21
{txt}
{com}. * tab treatment q3enew
. tab q3e q3enew

           {txt}{c |}          q3e
       q3e {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}        14         11 {txt}{c |}{res}        25 
{txt}         1 {c |}{res}       141         53 {txt}{c |}{res}       194 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       155         64 {txt}{c |}{res}       219 
{txt}
{com}. 
. // merge with baseline & endline
. use "$path_data/original_raw_score", clear
{txt}
{com}. keep student_no DT_score_pre cpcs_pre rosen_pre
{txt}
{com}. save "$path_data/temp/rawscore", replace
{txt}{p 0 4 2}
file {bf}
/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/data/temp/rawscore.dta{rm}
saved
{p_end}

{com}. 
. use "$path_data/temp/followup_student_data", clear
{txt}
{com}. drop _merge 
{txt}
{com}. merge 1:1 student_no using "$path_data/original_main"
{res}{txt}(label {bf:{txt}_merge} already defined)

{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}             812
{txt}{col 9}from master{col 30}{res}              44{txt}  (_merge==1)
{col 9}from using{col 30}{res}             768{txt}  (_merge==2)

{col 5}Matched{col 30}{res}             243{txt}  (_merge==3)
{col 5}{hline 41}

{com}. rename _merge _merge_base_character
{res}{txt}
{com}. merge 1:1 student_no using "$path_data/temp/rawscore"
{res}
{txt}{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}              44
{txt}{col 9}from master{col 30}{res}              44{txt}  (_merge==1)
{col 9}from using{col 30}{res}               0{txt}  (_merge==2)

{col 5}Matched{col 30}{res}           1,011{txt}  (_merge==3)
{col 5}{hline 41}

{com}. rename _merge _merge_base_score
{res}{txt}
{com}. tab treatment _merge_base_score

           {txt}{c |}  Matching
           {c |}   result
           {c |} from merge
 treatment {c |} Matched ( {c |}     Total
{hline 11}{c +}{hline 11}{c +}{hline 10}
         0 {c |}{res}       478 {txt}{c |}{res}       478 
{txt}         1 {c |}{res}       526 {txt}{c |}{res}       526 
{txt}{hline 11}{c +}{hline 11}{c +}{hline 10}
     Total {c |}{res}     1,004 {txt}{c |}{res}     1,004 
{txt}
{com}. 
. gen attrition = 0 if _merge_base_character == 3
{txt}(812 missing values generated)

{com}. recode attrition (.=1)
{txt}(812 changes made to {bf:attrition})

{com}. tab attrition

  {txt}attrition {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        243       23.03       23.03
{txt}          1 {c |}{res}        812       76.97      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,055      100.00
{txt}
{com}. 
. // fix missing values using baseline information
. replace school_no = 18 if school_no == . & student_no == 1817
{txt}(1 real change made)

{com}. replace treatment = 1 if student_no == 1817
{txt}(1 real change made)

{com}. replace grade = 2 if student_no == 1817
{txt}(1 real change made)

{com}. replace branch1 = 0 if student_no == 1817
{txt}(1 real change made)

{com}. replace branch2 = 0 if student_no == 1817
{txt}(1 real change made)

{com}. replace branch3 = 1 if student_no == 1817
{txt}(1 real change made)

{com}. replace branch4 = 0 if student_no == 1817
{txt}(1 real change made)

{com}. 
. save "$path_data/temp/student_unbalance", replace
{txt}{p 0 4 2}
file {bf}
/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/data/temp/student_unbalance.dta{rm}
saved
{p_end}

{com}. 
. 
. // keep balanced panel
. keep if attrition == 0
{txt}(812 observations deleted)

{com}. 
. save "$path_data/temp/endline_followup_student_data", replace
{txt}{p 0 4 2}
file {bf}
/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/data/temp/endline_followup_student_data.dta{rm}
saved
{p_end}

{com}. 
. 
. gen followup_cog = q6a1_correct + q6a2_correct + q6a3a_correct + q6a3b_correct + q6a4_correct + q6a5_correct
{txt}
{com}. 
. /// non-cog
> // positive: 2,3,5,7,10,11,12,17,18,20,21,22,23,25,26,27,28,29,32,33,34,36,37,39
. // positive-cog:1,13,14,19,24,
. // negative: 4,6,8,9,30,31,35,38,40
. 
. local q99 q6c1 q6c2 q6c3 q6c4 q6c5 q6c6 q6c7 q6c8 q6c9 q6c10 q6c11 q6c12 q6c13 q6c14 q6c15 q6c16 q6c17 q6c18 q6c19 q6c20 ///
> q6c21 q6c22 q6c23 q6c24 q6c25 q6c26 q6c27 q6c28 q6c29 q6c30 q6c31 q6c32 q6c33 q6c34 q6c35 q6c36 q6c37 q6c38 q6c39 q6c40 ///
> q8a1a q8a2a q8a3a q8a4a q8a5a
{txt}
{com}. 
. foreach y in `q99'{c -(}
{txt}  2{com}. replace `y'=.  if `y'==99
{txt}  3{com}. {c )-}
{txt}(1 real change made, 1 to missing)
(1 real change made, 1 to missing)
(1 real change made, 1 to missing)
(0 real changes made)
(3 real changes made, 3 to missing)
(2 real changes made, 2 to missing)
(0 real changes made)
(2 real changes made, 2 to missing)
(0 real changes made)
(0 real changes made)
(1 real change made, 1 to missing)
(1 real change made, 1 to missing)
(0 real changes made)
(0 real changes made)
(6 real changes made, 6 to missing)
(8 real changes made, 8 to missing)
(1 real change made, 1 to missing)
(12 real changes made, 12 to missing)
(12 real changes made, 12 to missing)
(5 real changes made, 5 to missing)
(1 real change made, 1 to missing)
(2 real changes made, 2 to missing)
(2 real changes made, 2 to missing)
(7 real changes made, 7 to missing)
(13 real changes made, 13 to missing)
(1 real change made, 1 to missing)
(2 real changes made, 2 to missing)
(1 real change made, 1 to missing)
(8 real changes made, 8 to missing)
(1 real change made, 1 to missing)
(0 real changes made)
(1 real change made, 1 to missing)
(0 real changes made)
(0 real changes made)
(26 real changes made, 26 to missing)
(16 real changes made, 16 to missing)
(0 real changes made)
(0 real changes made)
(1 real change made, 1 to missing)
(20 real changes made, 20 to missing)
(3 real changes made, 3 to missing)
(2 real changes made, 2 to missing)
(4 real changes made, 4 to missing)
(0 real changes made)
(1 real change made, 1 to missing)

{com}. 
. gen noncog4 = 5 - q6c4
{txt}
{com}. gen noncog6 = 5 - q6c6
{txt}(2 missing values generated)

{com}. gen noncog8 = 5 - q6c8
{txt}(2 missing values generated)

{com}. gen noncog9 = 5 - q6c9
{txt}
{com}. gen noncog30 = 5 - q6c30
{txt}(1 missing value generated)

{com}. gen noncog31 = 5 - q6c31
{txt}
{com}. gen noncog35 = 5 - q6c35
{txt}(26 missing values generated)

{com}. gen noncog38 = 5 - q6c38
{txt}
{com}. gen noncog40 = 5 - q6c40
{txt}(20 missing values generated)

{com}. 
. gen followup_noncog = q6c1+q6c2+q6c3+noncog4+q6c5+noncog6+q6c7+noncog8+noncog9+q6c10+q6c11+q6c12+q6c13+q6c14+q6c17+q6c18+q6c19+q6c20+q6c21+q6c22+q6c23+q6c24+q6c25+q6c26+q6c27+q6c28+q6c29+noncog30+noncog31+q6c32+q6c33+q6c34+noncog35+q6c36+q6c37+noncog38+noncog40+q6c39
{txt}(64 missing values generated)

{com}. gen followup_noncog2 = q6c2+q6c3+noncog4+q6c5+noncog6+q6c7+noncog8+noncog9+q6c10+q6c11+q6c12+q6c17+q6c18+q6c20+q6c21+q6c22+q6c23+q6c25+q6c26+q6c27+q6c28+q6c29+noncog30+noncog31+q6c32+q6c33+q6c34+noncog35+q6c36+q6c37+noncog38+noncog40+q6c39
{txt}(63 missing values generated)

{com}. 
. sum followup_noncog followup_noncog2

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
followup_n~g {c |}{res}        179    64.49721    16.11419         31        111
{txt}followup_n~2 {c |}{res}        180    55.78889    14.00558         25         94
{txt}
{com}. 
. replace followup_noncog = 190 - followup_noncog
{txt}(179 real changes made)

{com}. replace followup_noncog2 = 180 - followup_noncog2
{txt}(179 real changes made)

{com}. 
. gen RSES = 40 - q6c2 - q6c3 - noncog4 - noncog6 - noncog8 - noncog9 - q6c10 - q6c11
{txt}(7 missing values generated)

{com}. gen CPCS = 50 - q6c2 - q6c3 - noncog4 - q6c5 - noncog6 -q6c7 - noncog8 - noncog9 - q6c10 - q6c11
{txt}(7 missing values generated)

{com}. 
. /// variables for study situation
> gen tutor = 1 if q3a == 1
{txt}(149 missing values generated)

{com}. gen study_other = 1 if q4a == 1
{txt}(134 missing values generated)

{com}. gen study_affect_covid = 1 if q9a21 == 1
{txt}(90 missing values generated)

{com}. gen hometutoring = 1 if q9a2a1 == 1
{txt}(215 missing values generated)

{com}. gen onlineclass = 1 if q9a2a2 == 1
{txt}(223 missing values generated)

{com}. gen studymyself = 1 if q9a2a3 == 1
{txt}(115 missing values generated)

{com}. gen parentsteach = 1 if q9a2a4 == 1
{txt}(220 missing values generated)

{com}. recode tutor study_other study_affect_covid hometutoring onlineclass studymyself parentsteach (.=0)
{txt}(149 changes made to {bf:tutor})
(134 changes made to {bf:study_other})
(90 changes made to {bf:study_affect_covid})
(215 changes made to {bf:hometutoring})
(223 changes made to {bf:onlineclass})
(115 changes made to {bf:studymyself})
(220 changes made to {bf:parentsteach})

{com}. 
. /// other variable
> gen phone_survey = 1 if q1a0 == 2
{txt}(184 missing values generated)

{com}. recode phone_survey (.=0)
{txt}(184 changes made to {bf:phone_survey})

{com}. 
. /// Standardization
> egen DT_score_pre_mean = mean(DT_score_pre)
{txt}
{com}. egen DT_score_pre_sd = sd(DT_score_pre)
{txt}
{com}. gen DT_score_pre_std = (DT_score_pre-DT_score_pre_mean)/DT_score_pre_sd
{txt}(4 missing values generated)

{com}. drop DT_score_pre_mean DT_score_pre_sd 
{txt}
{com}. 
. egen cpcs_pre_mean = mean(cpcs_pre)
{txt}
{com}. egen cpcs_pre_sd = sd(cpcs_pre)
{txt}
{com}. gen cpcs_pre_std = (cpcs_pre-cpcs_pre_mean)/cpcs_pre_sd
{txt}
{com}. drop cpcs_pre_mean cpcs_pre_sd 
{txt}
{com}. 
. egen rosen_pre_mean = mean(rosen_pre)
{txt}
{com}. egen rosen_pre_sd = sd(rosen_pre)
{txt}
{com}. gen rosen_pre_std = (rosen_pre-rosen_pre_mean)/rosen_pre_sd
{txt}
{com}. drop rosen_pre_mean rosen_pre_sd 
{txt}
{com}. 
. egen followup_cog_mean = mean(followup_cog)
{txt}
{com}. egen followup_cog_sd = sd(followup_cog)
{txt}
{com}. gen followup_cog_std = (followup_cog-followup_cog_mean)/followup_cog_sd
{txt}
{com}. drop followup_cog_mean followup_cog_sd 
{txt}
{com}. 
. egen followup_noncog_mean = mean(followup_noncog)
{txt}
{com}. egen followup_noncog_sd = sd(followup_noncog)
{txt}
{com}. gen followup_noncog_std = (followup_noncog - followup_noncog_mean)/followup_noncog_sd
{txt}(64 missing values generated)

{com}. drop followup_noncog_mean followup_noncog_sd 
{txt}
{com}. 
. egen CPCS_mean = mean(CPCS)
{txt}
{com}. egen CPCS_sd = sd(CPCS)
{txt}
{com}. gen CPCS_std = (CPCS - CPCS_mean)/CPCS_sd
{txt}(7 missing values generated)

{com}. drop CPCS_mean CPCS_sd 
{txt}
{com}. 
. egen RSES_mean = mean(RSES)
{txt}
{com}. egen RSES_sd = sd(RSES)
{txt}
{com}. gen RSES_std = (RSES-RSES_mean)/RSES_sd
{txt}(7 missing values generated)

{com}. drop RSES_mean RSES_sd 
{txt}
{com}. 
. /// missing
> gen DT_score_pre_std_missing_dummy = 1 if DT_score_pre_std == .
{txt}(239 missing values generated)

{com}. gen cpcs_pre_std_missing_dummy = 1 if cpcs_pre_std == .
{txt}(243 missing values generated)

{com}. gen rosen_pre_std_missing_dummy = 1 if rosen_pre_std == .
{txt}(243 missing values generated)

{com}. recode DT_score_pre_std_missing_dummy cpcs_pre_std_missing_dummy rosen_pre_std_missing_dummy (.=0)
{txt}(239 changes made to {bf:DT_score_pre_std_missing_dummy})
(243 changes made to {bf:cpcs_pre_std_missing_dummy})
(243 changes made to {bf:rosen_pre_std_missing_dummy})

{com}. 
. gen DT_score_pre_std_missing_0 = DT_score_pre_std if DT_score_pre_std_missing == 0
{txt}(4 missing values generated)

{com}. gen cpcs_pre_std_missing_0 = cpcs_pre_std if cpcs_pre_std != .
{txt}
{com}. gen rosen_pre_std_missing_0 = rosen_pre_std if rosen_pre_std != .
{txt}
{com}. recode DT_score_pre_std_missing_0 cpcs_pre_std_missing_0 rosen_pre_std_missing_0 (.=0)
{txt}(4 changes made to {bf:DT_score_pre_std_missing_0})
(0 changes made to {bf:cpcs_pre_std_missing_0})
(0 changes made to {bf:rosen_pre_std_missing_0})

{com}. 
. gen hyper = 1 if q7d2a == 1 & q7d2b == 2
{txt}(231 missing values generated)

{com}. replace hyper = 1 if q7d2a == 1 & q7d2b == 3
{txt}(10 real changes made)

{com}. replace hyper = 1 if q7d2a == 2 & q7d2b == 3
{txt}(13 real changes made)

{com}. gen hypernoinfo = 1 if q7d2a == .
{txt}(184 missing values generated)

{com}. recode hyper hypernoinfo (.=0)
{txt}(208 changes made to {bf:hyper})
(184 changes made to {bf:hypernoinfo})

{com}. replace hyper = . if hypernoinfo == 1
{txt}(59 real changes made, 59 to missing)

{com}. 
. 
. 
. /// merge teacher information
> merge 1:1 student_no using "$path_data/temp/teacher"
{res}
{txt}{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}             763
{txt}{col 9}from master{col 30}{res}               1{txt}  (_merge==1)
{col 9}from using{col 30}{res}             762{txt}  (_merge==2)

{col 5}Matched{col 30}{res}             242{txt}  (_merge==3)
{col 5}{hline 41}

{com}. rename _merge _merge_teacher
{res}{txt}
{com}. recode age_tchr(.=0)
{txt}(33 changes made to {bf:age_tchr})

{com}. gen age_tchr_missing_dummy = 1 if age_tchr == 0
{txt}(972 missing values generated)

{com}. recode age_tchr_missing_dummy(.=0)
{txt}(972 changes made to {bf:age_tchr_missing_dummy})

{com}. 
. 
. save "$path_data/temp/followup_student_baseline_score_missing_dummy", replace
{txt}{p 0 4 2}
file {bf}
/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/data/temp/followup_student_baseline_score_missing_dummy.dta{rm}
saved
{p_end}

{com}. 
. 
. 
. 
{txt}end of do-file

{com}. 
. do "$path_do/1_data_cleaning_parents.do"
{txt}
{com}. clear all
{res}{txt}
{com}. set more off
{txt}
{com}. 
. import excel "$path_data/followup_parents_master.xlsx", clear first
{res}{text}(593 vars, 230 obs)

{com}. 
. local q3888999 b3 b7 ///
> cm1_2 cm1_3 cm1_4 cm1_5 cm1_6 cm1_7 cm1_8 cm1_9 cm1_10 cm1_11 ///
> cm2_2 cm2_3 cm2_4 cm2_5 cm2_6 cm2_7 cm2_8 cm2_9 cm2_10 cm2_11 ///
> cm3_2 cm3_3 cm3_4 cm3_5 cm3_6 cm3_7 cm3_8 cm3_9 cm3_10 cm3_11 ///
> cm4_2 cm4_3 cm4_4 cm4_5 cm4_6 cm4_7 cm4_8 cm4_9 cm4_10 cm4_11 ///
> e2 f2_1
{txt}
{com}. 
. local yesno f1_1 f1_3 f2_1
{txt}
{com}. 
. local missingzero e9_1 e9_2 e9_3 e9_4 e9_5 e9_6 e9_7 e9_8 e9_9
{txt}
{com}. 
. foreach y in `q3888999'{c -(}
{txt}  2{com}. replace `y'=.  if `y'==3
{txt}  3{com}. replace `y'=.  if `y'==888
{txt}  4{com}. replace `y'=.  if `y'==999
{txt}  5{com}. {c )-}
{txt}(21 real changes made, 21 to missing)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(1 real change made, 1 to missing)
(3 real changes made, 3 to missing)
(0 real changes made)
(0 real changes made)
(5 real changes made, 5 to missing)
(0 real changes made)
(0 real changes made)
(5 real changes made, 5 to missing)
(0 real changes made)
(3 real changes made, 3 to missing)
(2 real changes made, 2 to missing)
(0 real changes made)
(1 real change made, 1 to missing)
(4 real changes made, 4 to missing)
(0 real changes made)
(4 real changes made, 4 to missing)
(1 real change made, 1 to missing)
(0 real changes made)
(2 real changes made, 2 to missing)
(2 real changes made, 2 to missing)
(0 real changes made)
(2 real changes made, 2 to missing)
(4 real changes made, 4 to missing)
(0 real changes made)
(8 real changes made, 8 to missing)
(5 real changes made, 5 to missing)
(0 real changes made)
(3 real changes made, 3 to missing)
(2 real changes made, 2 to missing)
(0 real changes made)
(0 real changes made)
(1 real change made, 1 to missing)
(0 real changes made)
(1 real change made, 1 to missing)
(3 real changes made, 3 to missing)
(0 real changes made)
(1 real change made, 1 to missing)
(2 real changes made, 2 to missing)
(0 real changes made)
(1 real change made, 1 to missing)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(3 real changes made, 3 to missing)
(0 real changes made)
(2 real changes made, 2 to missing)
(3 real changes made, 3 to missing)
(0 real changes made)
(1 real change made, 1 to missing)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(2 real changes made, 2 to missing)
(0 real changes made)
(4 real changes made, 4 to missing)
(4 real changes made, 4 to missing)
(0 real changes made)
(1 real change made, 1 to missing)
(4 real changes made, 4 to missing)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(1 real change made, 1 to missing)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(1 real change made, 1 to missing)
(1 real change made, 1 to missing)
(2 real changes made, 2 to missing)
(1 real change made, 1 to missing)
(1 real change made, 1 to missing)
(0 real changes made)
(1 real change made, 1 to missing)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(3 real changes made, 3 to missing)
(0 real changes made)
(0 real changes made)
(2 real changes made, 2 to missing)
(0 real changes made)
(2 real changes made, 2 to missing)
(0 real changes made)
(1 real change made, 1 to missing)
(1 real change made, 1 to missing)
(1 real change made, 1 to missing)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(119 real changes made, 119 to missing)
(1 real change made, 1 to missing)
(0 real changes made)
(3 real changes made, 3 to missing)
(0 real changes made)
(0 real changes made)

{com}. 
. foreach y in `yesno'{c -(}
{txt}  2{com}. replace `y'=0  if `y'==2
{txt}  3{com}. {c )-}
{txt}(1 real change made)
(212 real changes made)
(2 real changes made)

{com}. 
. foreach y in `missingzero'{c -(}
{txt}  2{com}. replace `y'=0  if `y'==.
{txt}  3{com}. {c )-}
{txt}(55 real changes made)
(109 real changes made)
(230 real changes made)
(22 real changes made)
(195 real changes made)
(227 real changes made)
(221 real changes made)
(204 real changes made)
(9 real changes made)

{com}. 
. gen hhmember = a3
{txt}
{com}. gen hhheadage = am1_3a if am1_4 == 1
{txt}(2 missing values generated)

{com}. replace hhheadage = am2_3a if am2_4 == 1
{txt}(0 real changes made)

{com}. replace hhheadage = am3_3a if am3_4 == 1
{txt}(2 real changes made)

{com}. replace hhheadage = am4_3a if am4_4 == 1
{txt}(0 real changes made)

{com}. gen hhheadedu = am1_6 if am1_4 == 1
{txt}(2 missing values generated)

{com}. replace hhheadedu = am2_6 if am2_4 == 1
{txt}(0 real changes made)

{com}. replace hhheadedu = am3_6 if am3_4 == 1
{txt}(1 real change made)

{com}. replace hhheadedu = am4_6 if am4_4 == 1
{txt}(0 real changes made)

{com}. 
. gen hhheadeduyear = hhheadedu
{txt}(1 missing value generated)

{com}. replace hhheadeduyear = 10 if hhheadedu == 11
{txt}(1 real change made)

{com}. replace hhheadeduyear = 0 if hhheadedu == 17
{txt}(51 real changes made)

{com}. replace hhheadeduyear = 18 if hhheadedu == 15
{txt}(1 real change made)

{com}. replace hhheadeduyear = . if hhheadedu == 888
{txt}(2 real changes made, 2 to missing)

{com}. replace hhheadeduyear = . if hhheadedu == 999
{txt}(1 real change made, 1 to missing)

{com}. 
. destring x1d x1f x1h , replace
{txt}x1d: all characters numeric; {res}replaced {txt}as {res}int
{txt}x1f: all characters numeric; {res}replaced {txt}as {res}long
{txt}(167 missing values generated)
{res}{txt}x1h: all characters numeric; {res}replaced {txt}as {res}int
{txt}(228 missing values generated)
{res}{txt}
{com}. 
. keep x1d x1f x1h hhmember hhheadage hhheadedu hhheadeduyear
{txt}
{com}. 
. preserve
{txt}
{com}. collapse (mean) hhmember hhheadage hhheadedu hhheadeduyear, by(x1d)
{res}{txt}
{com}. replace hhheadeduyear = . if hhheadeduyear == 4.5
{txt}(1 real change made, 1 to missing)

{com}. rename x1d student_no
{res}{txt}
{com}. save "$path_data/temp/endline_followup_parents_data_1stchild", replace
{txt}{p 0 4 2}
file {bf}
/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/data/temp/endline_followup_parents_data_1stchild.dta{rm}
saved
{p_end}

{com}. 
. restore
{txt}
{com}. preserve
{txt}
{com}. rename x1f student_no
{res}{txt}
{com}. drop if student_no == .
{txt}(167 observations deleted)

{com}. collapse (mean) hhmember hhheadage hhheadedu hhheadeduyear, by(student_no)
{res}{txt}
{com}. replace hhheadeduyear = . if hhheadeduyear == 4.5
{txt}(1 real change made, 1 to missing)

{com}. save "$path_data/temp/endline_followup_parents_data_2ndchild", replace
{txt}{p 0 4 2}
file {bf}
/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/data/temp/endline_followup_parents_data_2ndchild.dta{rm}
saved
{p_end}

{com}. 
. restore
{txt}
{com}. preserve
{txt}
{com}. rename x1h student_no
{res}{txt}
{com}. drop if student_no == .
{txt}(228 observations deleted)

{com}. collapse (mean) hhmember hhheadage hhheadedu hhheadeduyear, by(student_no)
{res}{txt}
{com}. replace hhheadeduyear = . if hhheadeduyear == 4.5
{txt}(0 real changes made)

{com}. save "$path_data/temp/endline_followup_parents_data_3rdchild", replace
{txt}{p 0 4 2}
file {bf}
/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/data/temp/endline_followup_parents_data_3rdchild.dta{rm}
saved
{p_end}

{com}. 
. 
. 
{txt}end of do-file

{com}. 
. do "$path_do/1_data_cleaning_merge.do"
{txt}
{com}. set more off
{txt}
{com}. clear all
{res}{txt}
{com}. 
. use "$path_data/temp/followup_student_baseline_score_missing_dummy", clear
{txt}
{com}. merge 1:1 student_no using "$path_data/temp/endline_followup_parents_data_1stchild"
{res}
{txt}{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}             789
{txt}{col 9}from master{col 30}{res}             783{txt}  (_merge==1)
{col 9}from using{col 30}{res}               6{txt}  (_merge==2)

{col 5}Matched{col 30}{res}             222{txt}  (_merge==3)
{col 5}{hline 41}

{com}. rename _merge _merge_1st
{res}{txt}
{com}. rename hhmember hhmember_1st
{res}{txt}
{com}. rename hhheadage hhheadage_1st
{res}{txt}
{com}. rename hhheadeduyear hhheadeduyear_1st
{res}{txt}
{com}. 
. merge 1:1 student_no using "$path_data/temp/endline_followup_parents_data_2ndchild"
{res}{txt}{p 0 7 2}
(variable
{bf:student_no} was {bf:float}, now {bf:double} to accommodate using data's values)
{p_end}

{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}           1,025
{txt}{col 9}from master{col 30}{res}             987{txt}  (_merge==1)
{col 9}from using{col 30}{res}              38{txt}  (_merge==2)

{col 5}Matched{col 30}{res}              24{txt}  (_merge==3)
{col 5}{hline 41}

{com}. rename _merge _merge_2nd
{res}{txt}
{com}. rename hhmember hhmember_2nd
{res}{txt}
{com}. rename hhheadage hhheadage_2nd
{res}{txt}
{com}. rename hhheadeduyear hhheadeduyear_2nd
{res}{txt}
{com}. 
. merge 1:1 student_no using "$path_data/temp/endline_followup_parents_data_3rdchild"
{res}
{txt}{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}           1,047
{txt}{col 9}from master{col 30}{res}           1,047{txt}  (_merge==1)
{col 9}from using{col 30}{res}               0{txt}  (_merge==2)

{col 5}Matched{col 30}{res}               2{txt}  (_merge==3)
{col 5}{hline 41}

{com}. rename _merge _merge_3rd
{res}{txt}
{com}. rename hhmember hhmember_3rd
{res}{txt}
{com}. rename hhheadage hhheadage_3rd
{res}{txt}
{com}. rename hhheadeduyear hhheadeduyear_3rd
{res}{txt}
{com}. 
. recode hhmember* hhheadage* hhheadeduyear* (. = 0) 
{txt}(821 changes made to {bf:hhmember_1st})
(987 changes made to {bf:hhmember_2nd})
(1,047 changes made to {bf:hhmember_3rd})
(822 changes made to {bf:hhheadage_1st})
(988 changes made to {bf:hhheadage_2nd})
(1,047 changes made to {bf:hhheadage_3rd})
(826 changes made to {bf:hhheadeduyear_1st})
(991 changes made to {bf:hhheadeduyear_2nd})
(1,047 changes made to {bf:hhheadeduyear_3rd})

{com}. 
. gen hhmember = hhmember_1st + hhmember_2nd + hhmember_3rd
{txt}
{com}. gen hhheadage = hhheadage_1st + hhheadage_2nd + hhheadage_3rd
{txt}
{com}. gen hhheadeduyear = hhheadeduyear_1st + hhheadeduyear_2nd + hhheadeduyear_3rd
{txt}
{com}. 
. keep if attrition == 0
{txt}(806 observations deleted)

{com}. 
. save "$path_data/temp/followup_student_parents_matched", replace
{txt}{p 0 4 2}
file {bf}
/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/data/temp/followup_student_parents_matched.dta{rm}
saved
{p_end}

{com}. 
{txt}end of do-file

{com}. 
. *** run the codes for outputs.
. 
. 
. do "$path_do/2_table_1.do"
{txt}
{com}. * This is the do file to create "Table 1. Summary Statistics"
. set seed 1
{txt}
{com}. 
. use "$path_data/temp/followup_student_parents_matched", clear
{txt}
{com}. 
. corr rosen_pre_std cpcs_pre_std
{txt}(obs=243)

             {c |} rosen_~d cpcs_p~d
{hline 13}{c +}{hline 18}
rosen_pre_~d {c |}{res}   1.0000
{txt}cpcs_pre_std {c |}{res}   0.9026   1.0000

{txt}
{com}. corr RSES_std CPCS_std
{txt}(obs=236)

             {c |} RSES_std CPCS_std
{hline 13}{c +}{hline 18}
    RSES_std {c |}{res}   1.0000
    {txt}CPCS_std {c |}{res}   0.9701   1.0000

{txt}
{com}. 
. 
. /// Varable Selection
> /// Baseline
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat DT_score_pre_std rosen_pre_std cpcs_pre_std if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_bl = r(StatTotal)
{txt}  5{com}. 
. tabstat DT_score_pre_std rosen_pre_std cpcs_pre_std if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_bl = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}      144       145       145
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   DT_score_p~d  rosen_pre_~d  cpcs_pre_std
N {res}          144           145           145
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}       95        98        98
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   DT_score_p~d  rosen_pre_~d  cpcs_pre_std
N {res}           95            98            98
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res}-.0313509  .0382918  .1345164
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      DT_score_p~d  rosen_pre_~d  cpcs_pre_std
Mean {res}   -.03135095     .03829184      .1345164
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .0475214 -.0566567 -.1990291
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      DT_score_p~d  rosen_pre_~d  cpcs_pre_std
Mean {res}    .04752144    -.05665667    -.19902912
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} 1.023177  .9748496  .9271749
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    DT_score_p~d  rosen_pre_~d  cpcs_pre_std
SD {res}    1.0231772     .97484957     .92717486
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} .9672202  1.038561  1.073121
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    DT_score_p~d  rosen_pre_~d  cpcs_pre_std
SD {res}    .96722024      1.038561     1.0731214
{reset}
{com}. 
. matrix n_bl = J(1,3,.)
{txt}
{com}. forvalues i = 1/3 {c -(}
{txt}  2{com}.         matrix n_bl[1,`i'] = n_tr_bl[1,`i'] + n_ct_bl[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in DT_score_pre_std rosen_pre_std cpcs_pre_std{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}239
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 32
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  2
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.5
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        DT_score_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0788724{col 38}{space 1}  -0.38{col 46}{space 3}0.698{col 54}{space 3}-.5187522{col 66}{space 3}  .379723
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}           rosen_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0949485{col 38}{space 1}   0.47{col 46}{space 3}0.646{col 54}{space 3}-.3291105{col 66}{space 3} .5285112
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}            cpcs_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3335455{col 38}{space 1}   1.82{col 46}{space 3}0.074{col 54}{space 3}-.0352127{col 66}{space 3} .7279622
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. /// Family
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat hhmember hhheadage hhheadeduyear if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_parent = r(StatTotal)
{txt}  5{com}. 
. tabstat hhmember hhheadage hhheadeduyear if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_parent = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}      145       145       145
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
       hhmember     hhheadage  hhheadeduy~r
N {res}          145           145           145
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}       98        98        98
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
       hhmember     hhheadage  hhheadeduy~r
N {res}           98            98            98
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res} 4.510345  46.57241  2.331034
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
          hhmember     hhheadage  hhheadeduy~r
Mean {res}    4.5103448     46.572414     2.3310345
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res} 4.265306  46.68878  3.163265
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
          hhmember     hhheadage  hhheadeduy~r
Mean {res}    4.2653061     46.688776     3.1632653
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} 1.280827   9.03907  2.995495
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
        hhmember     hhheadage  hhheadeduy~r
SD {res}    1.2808268     9.0390702     2.9954947
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} 1.197515  9.408681  3.530993
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
        hhmember     hhheadage  hhheadeduy~r
SD {res}    1.1975148     9.4086808     3.5309935
{reset}
{com}. 
. matrix n_parent = J(1,3,.)
{txt}
{com}. forvalues i = 1/3 {c -(}
{txt}  2{com}.         matrix n_parent[1,`i'] = n_tr_parent[1,`i'] + n_ct_parent[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in hhmember hhheadage hhheadeduyear{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                hhmember{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2450387{col 38}{space 1}   1.29{col 46}{space 3}0.202{col 54}{space 3}-.1468311{col 66}{space 3} .6636916
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}               hhheadage{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.1163617{col 38}{space 1}  -0.07{col 46}{space 3}0.964{col 54}{space 3}-3.402025{col 66}{space 3} 3.385659
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:29})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}           hhheadeduyear{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.8322308{col 38}{space 1}  -2.22{col 46}{space 3}0.036{col 54}{space 3}-1.594462{col 66}{space 3}-.0665917
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. 
. 
. /// School　attendance
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat q2a q2b q2c q2h if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_school = r(StatTotal)
{txt}  5{com}. 
. tabstat q2a q2b q2c q2h if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_school = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:N} {...}
{c |}{...}
 {res}      145       145       145       145
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
   q2a  q2b  q2c  q2h
N {res} 145  145  145  145
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:N} {...}
{c |}{...}
 {res}       98        98        98        98
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
   q2a  q2b  q2c  q2h
N {res}  98   98   98   98
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .5517241  9.606897   .062069  .3793103
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
            q2a        q2b        q2c        q2h
Mean {res} .55172414  9.6068966  .06206897  .37931034
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .5306122  9.602041  .0408163  .4489796
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
            q2a        q2b        q2c        q2h
Mean {res} .53061224  9.6020408  .04081633  .44897959
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:SD} {...}
{c |}{...}
 {res} .4990412  1.029405  .2421171  .4868973
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
          q2a        q2b        q2c        q2h
SD {res} .49904123  1.0294048   .2421171  .48689728
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:SD} {...}
{c |}{...}
 {res} .5016279  .8703571  .1988818  .4999474
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
          q2a        q2b        q2c        q2h
SD {res}  .5016279  .87035715  .19888179   .4999474
{reset}
{com}. 
. matrix n_school = J(1,4,.)
{txt}
{com}. forvalues i = 1/4 {c -(}
{txt}  2{com}.         matrix n_school[1,`i'] = n_tr_school[1,`i'] + n_ct_school[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in q2a q2b q2c q2h{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                     q2a{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0211119{col 38}{space 1}   0.25{col 46}{space 3}0.798{col 54}{space 3}-.1531988{col 66}{space 3} .1997139
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                     q2b{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0048557{col 38}{space 1}   0.03{col 46}{space 3}0.950{col 54}{space 3}-.3886809{col 66}{space 3} .3871116
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                     q2c{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0212526{col 38}{space 1}   0.56{col 46}{space 3}0.634{col 54}{space 3}-.0503967{col 66}{space 3} .0975416
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                     q2h{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0696692{col 38}{space 1}  -0.85{col 46}{space 3}0.410{col 54}{space 3}-.2515308{col 66}{space 3} .0998351
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. /// Other study variable
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat tutor study_other study_affect_covid hometutoring onlineclass studymyself parentsteach if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_study = r(StatTotal)
{txt}  5{com}. 
. tabstat tutor study_other study_affect_covid hometutoring onlineclass studymyself parentsteach if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_study = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s
{hline 9}{c +}{hline 50}
{ralign 8:N} {...}
{c |}{...}
 {res}      145       145       145       145       145
{txt}{hline 9}{c BT}{hline 50}

   Stats {...}
{c |}{...}
  studym~f  parent~h
{hline 9}{c +}{hline 20}
{ralign 8:N} {...}
{c |}{...}
 {res}      145       145
{txt}{hline 9}{c BT}{hline 20}
{res}
{txt}r(StatTotal)[1,7]
          tutor   study_other  study_affe~d  hometutoring
N {res}          145           145           145           145

{txt}    onlineclass   studymyself  parentsteach
N {res}          145           145           145
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s
{hline 9}{c +}{hline 50}
{ralign 8:N} {...}
{c |}{...}
 {res}       98        98        98        98        98
{txt}{hline 9}{c BT}{hline 50}

   Stats {...}
{c |}{...}
  studym~f  parent~h
{hline 9}{c +}{hline 20}
{ralign 8:N} {...}
{c |}{...}
 {res}       98        98
{txt}{hline 9}{c BT}{hline 20}
{res}
{txt}r(StatTotal)[1,7]
          tutor   study_other  study_affe~d  hometutoring
N {res}           98            98            98            98

{txt}    onlineclass   studymyself  parentsteach
N {res}           98            98            98
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s
{hline 9}{c +}{hline 50}
{ralign 8:Mean} {...}
{c |}{...}
 {res}  .337931   .462069  .6482759  .0965517  .0482759
{txt}{hline 9}{c BT}{hline 50}

   Stats {...}
{c |}{...}
  studym~f  parent~h
{hline 9}{c +}{hline 20}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .5241379  .0275862
{txt}{hline 9}{c BT}{hline 20}
{res}
{txt}r(StatTotal)[1,7]
             tutor   study_other  study_affe~d  hometutoring
Mean {res}    .33793103     .46206897     .64827586     .09655172

{txt}       onlineclass   studymyself  parentsteach
Mean {res}    .04827586     .52413793     .02758621
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s
{hline 9}{c +}{hline 50}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .4591837  .4285714  .6020408  .1428571  .1326531
{txt}{hline 9}{c BT}{hline 50}

   Stats {...}
{c |}{...}
  studym~f  parent~h
{hline 9}{c +}{hline 20}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .5306122  .1938776
{txt}{hline 9}{c BT}{hline 20}
{res}
{txt}r(StatTotal)[1,7]
             tutor   study_other  study_affe~d  hometutoring
Mean {res}    .45918367     .42857143     .60204082     .14285714

{txt}       onlineclass   studymyself  parentsteach
Mean {res}    .13265306     .53061224     .19387755
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s
{hline 9}{c +}{hline 50}
{ralign 8:SD} {...}
{c |}{...}
 {res} .4746445  .5002873  .4791635  .2963701  .2150915
{txt}{hline 9}{c BT}{hline 50}

   Stats {...}
{c |}{...}
  studym~f  parent~h
{hline 9}{c +}{hline 20}
{ralign 8:SD} {...}
{c |}{...}
 {res} .5011481  .1643517
{txt}{hline 9}{c BT}{hline 20}
{res}
{txt}r(StatTotal)[1,7]
           tutor   study_other  study_affe~d  hometutoring
SD {res}    .47464445     .50028727     .47916354     .29637012

{txt}     onlineclass   studymyself  parentsteach
SD {res}    .21509153     .50114811     .16435174
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s
{hline 9}{c +}{hline 50}
{ralign 8:SD} {...}
{c |}{...}
 {res} .5008934   .497416  .4919935  .3517262  .3409434
{txt}{hline 9}{c BT}{hline 50}

   Stats {...}
{c |}{...}
  studym~f  parent~h
{hline 9}{c +}{hline 20}
{ralign 8:SD} {...}
{c |}{...}
 {res} .5016279  .3973667
{txt}{hline 9}{c BT}{hline 20}
{res}
{txt}r(StatTotal)[1,7]
           tutor   study_other  study_affe~d  hometutoring
SD {res}    .50089337       .497416     .49199354     .35172623

{txt}     onlineclass   studymyself  parentsteach
SD {res}    .34094336      .5016279     .39736667
{reset}
{com}. 
. matrix n_study = J(1,8,.)
{txt}
{com}. forvalues i = 1/8 {c -(}
{txt}  2{com}.         matrix n_study[1,`i'] = n_tr_study[1,`i'] + n_ct_study[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in tutor study_other study_affect_covid hometutoring onlineclass studymyself parentsteach{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                   tutor{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.1212526{col 38}{space 1}  -1.69{col 46}{space 3}0.140{col 54}{space 3} -.289002{col 66}{space 3} .0428947
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}             study_other{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0334975{col 38}{space 1}   0.39{col 46}{space 3}0.698{col 54}{space 3}-.1530471{col 66}{space 3} .2129988
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}      study_affect_covid{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  .046235{col 38}{space 1}   0.56{col 46}{space 3}0.604{col 54}{space 3}-.1390047{col 66}{space 3} .2363162
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}            hometutoring{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0463054{col 38}{space 1}  -1.11{col 46}{space 3}0.276{col 54}{space 3}-.1304481{col 66}{space 3} .0428661
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:19})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}             onlineclass{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0843772{col 38}{space 1}  -1.92{col 46}{space 3}0.102{col 54}{space 3}-.1754451{col 66}{space 3} .0172149
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:29})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}             studymyself{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0064743{col 38}{space 1}  -0.08{col 46}{space 3}0.922{col 54}{space 3}-.1483442{col 66}{space 3} .1796675
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}            parentsteach{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.1662913{col 38}{space 1}  -3.85{col 46}{space 3}0.002{col 54}{space 3}-.2557626{col 66}{space 3}-.0627718
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. /// Cognitive
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat followup_cog_std if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_cog = r(StatTotal)
{txt}  5{com}. 
. tabstat followup_cog_std if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_cog = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}{ralign 12:Variable} {...}
{c |}         N
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res}      145
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
   followup_c~d
N {res}          145
{reset}
{ralign 12:Variable} {...}
{c |}         N
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res}       98
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
   followup_c~d
N {res}           98
{reset}
{ralign 12:Variable} {...}
{c |}      Mean
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res}-.0920409
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
      followup_c~d
Mean {res}   -.09204085
{reset}
{ralign 12:Variable} {...}
{c |}      Mean
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res} .1361831
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
      followup_c~d
Mean {res}    .13618309
{reset}
{ralign 12:Variable} {...}
{c |}        SD
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res} 1.070796
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
    followup_c~d
SD {res}     1.070796
{reset}
{ralign 12:Variable} {...}
{c |}        SD
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res} .8725076
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
    followup_c~d
SD {res}    .87250763
{reset}
{com}. 
. matrix n_cog = J(1,1,.)
{txt}
{com}. forvalues i = 1/1 {c -(}
{txt}  2{com}.         matrix n_cog[1,`i'] = n_tr_cog[1,`i'] + n_ct_cog[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2282239{col 38}{space 1}  -1.36{col 46}{space 3}0.178{col 54}{space 3}-.5949467{col 66}{space 3} .1042338
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}.         
. matrix r2_followup_cog_std_temp = r(table)
{txt}
{com}. 
. 
. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix r2_followup_cog_std_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix r2_followup_cog_std_mean[1,`j'] = r2_followup_cog_std_temp[1,`j']
{txt}  3{com}. * standard error
. * matrix r2_followup_cog_std_se[1,`j'] = r2_followup_cog_std_temp[2,`j']
. * p value
. matrix r2_followup_cog_std_pv[1,`j'] = r2_followup_cog_std_temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}.     
. /// Non cognitive
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat followup_noncog_std RSES_std CPCS_std if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_noncog = r(StatTotal)
{txt}  5{com}. 
. tabstat followup_noncog_std RSES_std CPCS_std if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_noncog = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}      105       140       140
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   followup_n~d      RSES_std      CPCS_std
N {res}          105           140           140
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}       74        96        96
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   followup_n~d      RSES_std      CPCS_std
N {res}           74            96            96
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .1969319  .1591241  .1745941
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      followup_n~d      RSES_std      CPCS_std
Mean {res}    .19693189      .1591241     .17459415
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res}-.2794302 -.2320565  -.254617
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      followup_n~d      RSES_std      CPCS_std
Mean {res}   -.27943024    -.23205648    -.25461705
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} 1.006158  1.022691  1.008304
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    followup_n~d      RSES_std      CPCS_std
SD {res}    1.0061577     1.0226907     1.0083041
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} .9279901  .9228443  .9357831
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    followup_n~d      RSES_std      CPCS_std
SD {res}    .92799012     .92284427     .93578307
{reset}
{com}. 
. matrix n_noncog = J(1,3,.)
{txt}
{com}. forvalues i = 1/3 {c -(}
{txt}  2{com}.         matrix n_noncog[1,`i'] = n_tr_noncog[1,`i'] + n_ct_noncog[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in followup_noncog_std RSES_std CPCS_std{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}30{text}.{text} done{text} ({result:31})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}179
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 32
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}5.6
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}     followup_noncog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .4763621{col 38}{space 1}   2.08{col 46}{space 3}0.058{col 54}{space 3}-.0144745{col 66}{space 3} .9682468
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:29})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}236
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.2
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3911806{col 38}{space 1}   2.02{col 46}{space 3}0.068{col 54}{space 3}-.0320917{col 66}{space 3} .7926849
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}236
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.2
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .4292112{col 38}{space 1}   2.26{col 46}{space 3}0.040{col 54}{space 3} .0113741{col 66}{space 3} .8020083
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. 
. /// Behavioral
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat hyper if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_hyper = r(StatTotal)
{txt}  5{com}. 
. tabstat hyper if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_hyper = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}{ralign 12:Variable} {...}
{c |}         N
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res}      113
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
   hyper
N {res}   113
{reset}
{ralign 12:Variable} {...}
{c |}         N
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res}       71
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
   hyper
N {res}    71
{reset}
{ralign 12:Variable} {...}
{c |}      Mean
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res} .2654867
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
          hyper
Mean {res} .26548673
{reset}
{ralign 12:Variable} {...}
{c |}      Mean
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res} .0704225
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
          hyper
Mean {res} .07042254
{reset}
{ralign 12:Variable} {...}
{c |}        SD
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res}  .443559
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
        hyper
SD {res} .44355905
{reset}
{ralign 12:Variable} {...}
{c |}        SD
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res} .2576789
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
        hyper
SD {res} .25767885
{reset}
{com}. 
. matrix n_hyper = J(1,1,.)
{txt}
{com}. forvalues i = 1/1 {c -(}
{txt}  2{com}.         matrix n_hyper[1,`i'] = n_tr_hyper[1,`i'] + n_ct_hyper[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in hyper{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment if hypernoinfo == 0, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:29})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}184
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}5.6
{col 69}{txt}max{col 72} = {res} 12
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                   hyper{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1950642{col 38}{space 1}   3.37{col 46}{space 3}0.004{col 54}{space 3} .0791866{col 66}{space 3} .3123475
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. 
. // significant level
. 
. local outcome DT_score_pre_std rosen_pre_std cpcs_pre_std hhmember hhheadage hhheadeduyear q2a q2c q2h tutor study_other followup_cog_std RSES_std CPCS_std
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}.                 if r2_`dep'_pv[1,1]<=0.01 {c -(}
{txt}  3{com}.                         local star_`dep' %3s "***"
{txt}  4{com}.                 {c )-}
{txt}  5{com}.                 else if (r2_`dep'_pv[1,1]>0.01) & (r2_`dep'_pv[1,1]<=0.05) {c -(}
{txt}  6{com}.                         local star_`dep' %2s "**"
{txt}  7{com}.                 {c )-}
{txt}  8{com}.                 else if (r2_`dep'_pv[1,1]>0.05) & (r2_`dep'_pv[1,1]<=0.10) {c -(}
{txt}  9{com}.                         local star_`dep' %1s "*"
{txt} 10{com}.                 {c )-}
{txt} 11{com}.                 else {c -(}
{txt} 12{com}.                         local star_`dep'  ""
{txt} 13{com}.                 {c )-}
{txt} 14{com}. {c )-} 
{txt}
{com}. 
. set seed 1
{txt}
{com}. rwolf DT_score_pre_std rosen_pre_std cpcs_pre_std hhmember hhheadage hhheadeduyear, indepvar(treatment) reps(1000)
Bootstrap replications (1000). This may take some time.
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Romano-Wolf step-down adjusted p-values


Independent variable:  treatment
Outcome variables:   DT_score_pre_std rosen_pre_std cpcs_pre_std hhmember
{col 22}hhheadage hhheadeduyear
Number of resamples: 1000


{hline 78}
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
{hline 20}+{hline 57}
   {txt}DT_score_pre_std {c |}    {res}0.5518             0.5495              0.8312
      {txt}rosen_pre_std {c |}    {res}0.4689             0.4496              0.8312
       {txt}cpcs_pre_std {c |}    {res}0.0105             0.0160              0.0589
           {txt}hhmember {c |}    {res}0.1345             0.1469              0.4635
          {txt}hhheadage {c |}    {res}0.9229             0.9201              0.9201
      {txt}hhheadeduyear {c |}    {res}0.0494             0.0500              0.2478
{hline 78}
{txt}
{com}. set seed 1
{txt}
{com}. rwolf q2a q2c q2h tutor study_other followup_cog_std RSES_std CPCS_std, indepvar(treatment) reps(1000)
Bootstrap replications (1000). This may take some time.
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Romano-Wolf step-down adjusted p-values


Independent variable:  treatment
Outcome variables:   q2a q2c q2h tutor study_other followup_cog_std RSES_std CPCS_std
Number of resamples: 1000


{hline 78}
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
{hline 20}+{hline 57}
                {txt}q2a {c |}    {res}0.7471             0.7493              0.7872
                {txt}q2c {c |}    {res}0.4722             0.4595              0.7872
                {txt}q2h {c |}    {res}0.2801             0.2717              0.5764
              {txt}tutor {c |}    {res}0.0573             0.0460              0.2298
        {txt}study_other {c |}    {res}0.6083             0.6234              0.7872
   {txt}followup_cog_std {c |}    {res}0.0809             0.0799              0.2697
           {txt}RSES_std {c |}    {res}0.0030             0.0030              0.0110
           {txt}CPCS_std {c |}    {res}0.0011             0.0020              0.0060
{hline 78}
{txt}
{com}. 
. 
. /// Table
> tempname hh2
{txt}
{com}. file open `hh2' using "$path_output/summary_stat.tex", write replace
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "% Author: Kazuma Takakura" _newline
{txt}
{com}. file write `hh2' "% Date: `c(current_date)'" _newline
{txt}
{com}. file write `hh2' "% Time: `c(current_time)'" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. 
. file write `hh2' "\begin{c -(}table{c )-}[h!]\footnotesize" _newline
{txt}
{com}. file write `hh2' "  \centering" _newline
{txt}
{com}. file write `hh2' "  \caption{c -(}Summary Statistics{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:sumstat{c )-}" _newline
{txt}
{com}. file write `hh2' "\scalebox{c -(}1{c )-}{c -(}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}threeparttable{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "\begin{c -(}tabular{c )-}{c -(}lccccc{c )-}\toprule" _newline
{txt}
{com}. 
.   
. file write `hh2' " Dependent Variable & Treatment &  Control  & Difference & N   \\\midrule\midrule" _newline
{txt}
{com}. file write `hh2' " Panel A: Baseline & & & &   \\ " _newline
{txt}
{com}. file write `hh2' " DT score^{c -(}a{c )-} & " %04.3f (mean_tr_bl[1,1]) " & " %04.3f (mean_ct_bl[1,1]) " & " %04.3f (r2_DT_score_pre_std_mean[1,1]) `star_DT_score_pre_std' " & " (n_bl[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_bl[1,1]) " ] & [ " %04.3f (sd_ct_bl[1,1]) " ] & ( " %04.3f (r2_DT_score_pre_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.831) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. 
. file write `hh2' " RSES score^{c -(}a{c )-} & " %04.3f (mean_tr_bl[1,2]) " & " %04.3f (mean_ct_bl[1,2]) " & " %04.3f (r2_rosen_pre_std_mean[1,1]) `star_rosen_pre_std' " & "  (n_bl[1,2]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_bl[1,2]) " ] & [ " %04.3f (sd_ct_bl[1,2]) " ] & ( " %04.3f (r2_rosen_pre_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.831) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " CPCS score^{c -(}a{c )-} & " %04.3f (mean_tr_bl[1,3]) " & " %04.3f (mean_ct_bl[1,3]) " & " %04.3f (r2_cpcs_pre_std_mean[1,1]) `star_cpcs_pre_std' " & "  (n_bl[1,3]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_bl[1,3]) " ] & [ " %04.3f (sd_ct_bl[1,3]) " ] & ( " %04.3f (r2_cpcs_pre_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.059) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Household size & " %04.3f (mean_tr_parent[1,1]) " & " %04.3f (mean_ct_parent[1,1]) " & " %04.3f (r2_hhmember_mean[1,1]) `star_hhmember'  " & " (n_parent[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_parent[1,1]) " ] & [ " %04.3f (sd_ct_parent[1,1]) " ] & ( " %04.3f (r2_hhmember_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.464) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Household head age & " %04.3f (mean_tr_parent[1,2]) " & " %04.3f (mean_ct_parent[1,2]) " & " %04.3f (r2_hhheadage_mean[1,1]) `star_hhheadage' " & "  (n_parent[1,2]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_parent[1,2]) " ] & [ " %04.3f (sd_ct_parent[1,2]) " ] & ( " %04.3f (r2_hhheadage_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.920) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Household head education & " %04.3f (mean_tr_parent[1,3]) " & " %04.3f (mean_ct_parent[1,3]) " & " %04.3f (r2_hhheadeduyear_mean[1,1]) `star_hhheadeduyear' " & "  (n_parent[1,3]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_parent[1,3]) " ] & [ " %04.3f (sd_ct_parent[1,3]) " ] & ( " %04.3f (r2_hhheadeduyear_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.248) " \{c )-} &   \\ " _newline
{txt}
{com}. file write `hh2' " \\ "_newline
{txt}
{com}. 
. file write `hh2' " Panel B: Follow-up & & & &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " School attendance & " %04.3f (mean_tr_school[1,1]) " & " %04.3f (mean_ct_school[1,1]) " & " %04.3f (r2_q2a_mean[1,1]) `star_q2a' " & " (n_school[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_school[1,1]) " ] & [ " %04.3f (sd_ct_school[1,1]) " ] & ( " %04.3f (r2_q2a_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.787) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Grade repeat & " %04.3f (mean_tr_school[1,3]) " & " %04.3f (mean_ct_school[1,3]) " & " %04.3f (r2_q2c_mean[1,1]) `star_q2c' " & "  (n_school[1,3]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_school[1,3]) " ] & [ " %04.3f (sd_ct_school[1,3]) " ] & ( " %04.3f (r2_q2c_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.787) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Drop out & " %04.3f (mean_tr_school[1,4]) " & " %04.3f (mean_ct_school[1,4]) " & " %04.3f (r2_q2h_mean[1,1]) `star_q2h'  " & "  (n_school[1,4]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_school[1,4]) " ] & [ " %04.3f (sd_ct_school[1,4]) " ] & ( " %04.3f (r2_q2h_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.576) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Tutoring & " %04.3f (mean_tr_study[1,1]) " & " %04.3f (mean_ct_study[1,1]) " & " %04.3f (r2_tutor_mean[1,1]) `star_tutor'  " & " (n_study[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_study[1,1]) " ] & [ " %04.3f (sd_ct_study[1,1]) " ] & ( " %04.3f (r2_tutor_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.230) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Self-study & " %04.3f (mean_tr_study[1,2]) " & " %04.3f (mean_ct_study[1,2]) " & " %04.3f (r2_study_other_mean[1,1]) `star_study_other' " & "  (n_study[1,2]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_study[1,2]) " ] & [ " %04.3f (sd_ct_study[1,2]) " ] & ( " %04.3f (r2_study_other_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.787) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Rapid math test score^{c -(}a{c )-} & " %04.3f (mean_tr_cog[1,1]) " & " %04.3f (mean_ct_cog[1,1]) " & " %04.3f (r2_followup_cog_std_mean[1,1]) `star_followup_cog_std'  "  & "  (n_cog[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_cog[1,1]) " ] & [ " %04.3f (sd_ct_cog[1,1]) " ] & ( " %04.3f (r2_followup_cog_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.270) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " RSES score^{c -(}a{c )-} & " %04.3f (mean_tr_noncog[1,2]) " & " %04.3f (mean_ct_noncog[1,2]) " & " %04.3f (r2_RSES_std_mean[1,1])   `star_RSES_std' " & " (n_noncog[1,2]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_noncog[1,2]) " ] & [ " %04.3f (sd_ct_noncog[1,2]) " ] & ( " %04.3f (r2_RSES_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.011) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " CPCS score^{c -(}a{c )-} & " %04.3f (mean_tr_noncog[1,3]) " & " %04.3f (mean_ct_noncog[1,3]) " & " %04.3f (r2_CPCS_std_mean[1,1])   `star_CPCS_std' "&  " (n_noncog[1,3]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_noncog[1,3]) " ] & [ " %04.3f (sd_ct_noncog[1,3]) " ] & ( " %04.3f (r2_CPCS_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.006) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. 
. file write `hh2' "\midrule" _newline
{txt}
{com}. file write `hh2' "\end{c -(}tabular{c )-}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\item (a) Variables are standardized using the average and variance of the whole follow-up sample in the February 2022 survey. " _newline
{txt}
{com}. file write `hh2' "\item (b) Standard deviations are reported in square brackets.  " _newline
{txt}
{com}. file write `hh2' "\item (c) Wild clustered bootstrap p-values are reported within parentheses. Clusters are schools at the baseline. There are 34 clusters. " _newline
{txt}
{com}. file write `hh2' "\item (d) Romano-Wolf multiple hypothesis testing p-values are reported in curly brackets. This test is conducted separately for the baseline variables and the follow-up variables." _newline
{txt}
{com}. file write `hh2' "\item (e) Statistical significance is indicated by stars based on the wild clustered bootstrap p-values reported in parentheses: $*$ denotes significance at the 10\% level, $∗∗$ at the 5\% level, and $∗∗∗$ at the 1\% level.  " _newline
{txt}
{com}. file write `hh2' "\end{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}threeparttable{c )-}" _newline
{txt}
{com}. file write `hh2' "{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}table{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. file close `hh2'
{txt}
{com}. 
{txt}end of do-file

{com}. 
{txt}end of do-file

{com}. do "/var/folders/mb/s9qljd594gbfyhl2dqnnwlcw0000gn/T//SD56186.000000"
{txt}
{com}. * This do-file making summary stat
. 
. set more off
{txt}
{com}. clear all
{res}{txt}
{com}. 
. global wd "/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up"
{txt}
{com}. global dd "$wd/data"
{txt}
{com}. 
. use "$dd/followup_student_parents_matched", clear
{txt}
{com}. local pardir "/Users/takakurakazuma/Dropbox/Apps/Overleaf/kumon_followup/table"
{txt}
{com}. 
. corr rosen_pre_std cpcs_pre_std
{txt}(obs=243)

             {c |} rosen_~d cpcs_p~d
{hline 13}{c +}{hline 18}
rosen_pre_~d {c |}{res}   1.0000
{txt}cpcs_pre_std {c |}{res}   0.9026   1.0000

{txt}
{com}. corr RSES_std CPCS_std
{txt}(obs=236)

             {c |} RSES_std CPCS_std
{hline 13}{c +}{hline 18}
    RSES_std {c |}{res}   1.0000
    {txt}CPCS_std {c |}{res}   0.9701   1.0000

{txt}
{com}. 
. 
. /// Varable Selection
> /// Baseline
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat DT_score_pre_std rosen_pre_std cpcs_pre_std if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_bl = r(StatTotal)
{txt}  5{com}. 
. tabstat DT_score_pre_std rosen_pre_std cpcs_pre_std if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_bl = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}      144       145       145
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   DT_score_p~d  rosen_pre_~d  cpcs_pre_std
N {res}          144           145           145
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}       95        98        98
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   DT_score_p~d  rosen_pre_~d  cpcs_pre_std
N {res}           95            98            98
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res}-.0313509  .0382918  .1345164
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      DT_score_p~d  rosen_pre_~d  cpcs_pre_std
Mean {res}   -.03135095     .03829184      .1345164
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .0475214 -.0566567 -.1990291
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      DT_score_p~d  rosen_pre_~d  cpcs_pre_std
Mean {res}    .04752144    -.05665667    -.19902912
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} 1.023177  .9748496  .9271749
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    DT_score_p~d  rosen_pre_~d  cpcs_pre_std
SD {res}    1.0231772     .97484957     .92717486
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} .9672202  1.038561  1.073121
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    DT_score_p~d  rosen_pre_~d  cpcs_pre_std
SD {res}    .96722024      1.038561     1.0731214
{reset}
{com}. 
. matrix n_bl = J(1,3,.)
{txt}
{com}. forvalues i = 1/3 {c -(}
{txt}  2{com}.         matrix n_bl[1,`i'] = n_tr_bl[1,`i'] + n_ct_bl[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in DT_score_pre_std rosen_pre_std cpcs_pre_std{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}239
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 32
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  2
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.5
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        DT_score_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0788724{col 38}{space 1}  -0.38{col 46}{space 3}0.652{col 54}{space 3}-.5022926{col 66}{space 3} .3705235
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}           rosen_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0949485{col 38}{space 1}   0.47{col 46}{space 3}0.692{col 54}{space 3}-.3579905{col 66}{space 3} .5273619
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}            cpcs_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3335455{col 38}{space 1}   1.82{col 46}{space 3}0.106{col 54}{space 3} -.079527{col 66}{space 3} .7116932
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/mb/s9qljd594gbfyhl2dqnnwlcw0000gn/T//SD56186.000000"
{txt}
{com}. * This do-file making summary stat
. 
. set more off
{txt}
{com}. clear all
{res}{txt}
{com}. 
. global wd "/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up"
{txt}
{com}. global dd "$wd/data"
{txt}
{com}. 
. use "$dd/followup_student_parents_matched", clear
{txt}
{com}. local pardir "/Users/takakurakazuma/Dropbox/Apps/Overleaf/kumon_followup/table"
{txt}
{com}. 
. corr rosen_pre_std cpcs_pre_std
{txt}(obs=243)

             {c |} rosen_~d cpcs_p~d
{hline 13}{c +}{hline 18}
rosen_pre_~d {c |}{res}   1.0000
{txt}cpcs_pre_std {c |}{res}   0.9026   1.0000

{txt}
{com}. corr RSES_std CPCS_std
{txt}(obs=236)

             {c |} RSES_std CPCS_std
{hline 13}{c +}{hline 18}
    RSES_std {c |}{res}   1.0000
    {txt}CPCS_std {c |}{res}   0.9701   1.0000

{txt}
{com}. 
. set seed 1
{txt}
{com}. /// Varable Selection
> /// Baseline
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat DT_score_pre_std rosen_pre_std cpcs_pre_std if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_bl = r(StatTotal)
{txt}  5{com}. 
. tabstat DT_score_pre_std rosen_pre_std cpcs_pre_std if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_bl = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}      144       145       145
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   DT_score_p~d  rosen_pre_~d  cpcs_pre_std
N {res}          144           145           145
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}       95        98        98
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   DT_score_p~d  rosen_pre_~d  cpcs_pre_std
N {res}           95            98            98
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res}-.0313509  .0382918  .1345164
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      DT_score_p~d  rosen_pre_~d  cpcs_pre_std
Mean {res}   -.03135095     .03829184      .1345164
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .0475214 -.0566567 -.1990291
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      DT_score_p~d  rosen_pre_~d  cpcs_pre_std
Mean {res}    .04752144    -.05665667    -.19902912
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} 1.023177  .9748496  .9271749
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    DT_score_p~d  rosen_pre_~d  cpcs_pre_std
SD {res}    1.0231772     .97484957     .92717486
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} .9672202  1.038561  1.073121
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    DT_score_p~d  rosen_pre_~d  cpcs_pre_std
SD {res}    .96722024      1.038561     1.0731214
{reset}
{com}. 
. matrix n_bl = J(1,3,.)
{txt}
{com}. forvalues i = 1/3 {c -(}
{txt}  2{com}.         matrix n_bl[1,`i'] = n_tr_bl[1,`i'] + n_ct_bl[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in DT_score_pre_std rosen_pre_std cpcs_pre_std{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}239
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 32
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  2
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.5
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        DT_score_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0788724{col 38}{space 1}  -0.38{col 46}{space 3}0.698{col 54}{space 3}-.5187522{col 66}{space 3}  .379723
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}           rosen_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0949485{col 38}{space 1}   0.47{col 46}{space 3}0.646{col 54}{space 3}-.3291105{col 66}{space 3} .5285112
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}            cpcs_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3335455{col 38}{space 1}   1.82{col 46}{space 3}0.074{col 54}{space 3}-.0352127{col 66}{space 3} .7279622
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/mb/s9qljd594gbfyhl2dqnnwlcw0000gn/T//SD56186.000000"
{txt}
{com}. * This do-file making summary stat
. 
. set more off
{txt}
{com}. clear all
{res}{txt}
{com}. 
. global wd "/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up"
{txt}
{com}. global dd "$wd/data"
{txt}
{com}. 
. use "$dd/followup_student_parents_matched", clear
{txt}
{com}. local pardir "/Users/takakurakazuma/Dropbox/Apps/Overleaf/kumon_followup/table"
{txt}
{com}. 
. corr rosen_pre_std cpcs_pre_std
{txt}(obs=243)

             {c |} rosen_~d cpcs_p~d
{hline 13}{c +}{hline 18}
rosen_pre_~d {c |}{res}   1.0000
{txt}cpcs_pre_std {c |}{res}   0.9026   1.0000

{txt}
{com}. corr RSES_std CPCS_std
{txt}(obs=236)

             {c |} RSES_std CPCS_std
{hline 13}{c +}{hline 18}
    RSES_std {c |}{res}   1.0000
    {txt}CPCS_std {c |}{res}   0.9701   1.0000

{txt}
{com}. 
. /// Varable Selection
> /// Baseline
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat DT_score_pre_std rosen_pre_std cpcs_pre_std if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_bl = r(StatTotal)
{txt}  5{com}. 
. tabstat DT_score_pre_std rosen_pre_std cpcs_pre_std if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_bl = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}      144       145       145
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   DT_score_p~d  rosen_pre_~d  cpcs_pre_std
N {res}          144           145           145
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}       95        98        98
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   DT_score_p~d  rosen_pre_~d  cpcs_pre_std
N {res}           95            98            98
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res}-.0313509  .0382918  .1345164
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      DT_score_p~d  rosen_pre_~d  cpcs_pre_std
Mean {res}   -.03135095     .03829184      .1345164
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .0475214 -.0566567 -.1990291
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      DT_score_p~d  rosen_pre_~d  cpcs_pre_std
Mean {res}    .04752144    -.05665667    -.19902912
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} 1.023177  .9748496  .9271749
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    DT_score_p~d  rosen_pre_~d  cpcs_pre_std
SD {res}    1.0231772     .97484957     .92717486
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} .9672202  1.038561  1.073121
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    DT_score_p~d  rosen_pre_~d  cpcs_pre_std
SD {res}    .96722024      1.038561     1.0731214
{reset}
{com}. 
. matrix n_bl = J(1,3,.)
{txt}
{com}. forvalues i = 1/3 {c -(}
{txt}  2{com}.         matrix n_bl[1,`i'] = n_tr_bl[1,`i'] + n_ct_bl[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in DT_score_pre_std rosen_pre_std cpcs_pre_std{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}239
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 32
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  2
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.5
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        DT_score_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0788724{col 38}{space 1}  -0.38{col 46}{space 3}0.698{col 54}{space 3}-.5453221{col 66}{space 3} .3750789
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}           rosen_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0949485{col 38}{space 1}   0.47{col 46}{space 3}0.634{col 54}{space 3}-.3230894{col 66}{space 3}  .526451
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}            cpcs_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3335455{col 38}{space 1}   1.82{col 46}{space 3}0.086{col 54}{space 3}-.0353453{col 66}{space 3} .6981929
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/mb/s9qljd594gbfyhl2dqnnwlcw0000gn/T//SD56186.000000"
{txt}
{com}. * This do-file making summary stat
. 
. set more off
{txt}
{com}. clear all
{res}{txt}
{com}. 
. global wd "/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up"
{txt}
{com}. global dd "$wd/data"
{txt}
{com}. 
. use "$dd/followup_student_parents_matched", clear
{txt}
{com}. local pardir "/Users/takakurakazuma/Dropbox/Apps/Overleaf/kumon_followup/table"
{txt}
{com}. 
. corr rosen_pre_std cpcs_pre_std
{txt}(obs=243)

             {c |} rosen_~d cpcs_p~d
{hline 13}{c +}{hline 18}
rosen_pre_~d {c |}{res}   1.0000
{txt}cpcs_pre_std {c |}{res}   0.9026   1.0000

{txt}
{com}. corr RSES_std CPCS_std
{txt}(obs=236)

             {c |} RSES_std CPCS_std
{hline 13}{c +}{hline 18}
    RSES_std {c |}{res}   1.0000
    {txt}CPCS_std {c |}{res}   0.9701   1.0000

{txt}
{com}. 
. /// Varable Selection
> /// Baseline
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat DT_score_pre_std rosen_pre_std cpcs_pre_std if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_bl = r(StatTotal)
{txt}  5{com}. 
. tabstat DT_score_pre_std rosen_pre_std cpcs_pre_std if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_bl = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}      144       145       145
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   DT_score_p~d  rosen_pre_~d  cpcs_pre_std
N {res}          144           145           145
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}       95        98        98
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   DT_score_p~d  rosen_pre_~d  cpcs_pre_std
N {res}           95            98            98
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res}-.0313509  .0382918  .1345164
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      DT_score_p~d  rosen_pre_~d  cpcs_pre_std
Mean {res}   -.03135095     .03829184      .1345164
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .0475214 -.0566567 -.1990291
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      DT_score_p~d  rosen_pre_~d  cpcs_pre_std
Mean {res}    .04752144    -.05665667    -.19902912
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} 1.023177  .9748496  .9271749
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    DT_score_p~d  rosen_pre_~d  cpcs_pre_std
SD {res}    1.0231772     .97484957     .92717486
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} .9672202  1.038561  1.073121
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    DT_score_p~d  rosen_pre_~d  cpcs_pre_std
SD {res}    .96722024      1.038561     1.0731214
{reset}
{com}. 
. matrix n_bl = J(1,3,.)
{txt}
{com}. forvalues i = 1/3 {c -(}
{txt}  2{com}.         matrix n_bl[1,`i'] = n_tr_bl[1,`i'] + n_ct_bl[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in DT_score_pre_std rosen_pre_std cpcs_pre_std{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}239
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 32
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  2
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.5
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        DT_score_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0788724{col 38}{space 1}  -0.38{col 46}{space 3}0.696{col 54}{space 3}-.5187125{col 66}{space 3} .3644977
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}           rosen_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0949485{col 38}{space 1}   0.47{col 46}{space 3}0.696{col 54}{space 3}-.3450761{col 66}{space 3} .4971886
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}            cpcs_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3335455{col 38}{space 1}   1.82{col 46}{space 3}0.102{col 54}{space 3}-.0518928{col 66}{space 3} .7011423
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/mb/s9qljd594gbfyhl2dqnnwlcw0000gn/T//SD56186.000000"
{txt}
{com}. * This do-file making summary stat
. 
. set more off
{txt}
{com}. clear all
{res}{txt}
{com}. 
. global wd "/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up"
{txt}
{com}. global dd "$wd/data"
{txt}
{com}. 
. use "$dd/followup_student_parents_matched", clear
{txt}
{com}. local pardir "/Users/takakurakazuma/Dropbox/Apps/Overleaf/kumon_followup/table"
{txt}
{com}. 
. corr rosen_pre_std cpcs_pre_std
{txt}(obs=243)

             {c |} rosen_~d cpcs_p~d
{hline 13}{c +}{hline 18}
rosen_pre_~d {c |}{res}   1.0000
{txt}cpcs_pre_std {c |}{res}   0.9026   1.0000

{txt}
{com}. corr RSES_std CPCS_std
{txt}(obs=236)

             {c |} RSES_std CPCS_std
{hline 13}{c +}{hline 18}
    RSES_std {c |}{res}   1.0000
    {txt}CPCS_std {c |}{res}   0.9701   1.0000

{txt}
{com}. 
. set seed 123
{txt}
{com}. /// Varable Selection
> /// Baseline
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat DT_score_pre_std rosen_pre_std cpcs_pre_std if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_bl = r(StatTotal)
{txt}  5{com}. 
. tabstat DT_score_pre_std rosen_pre_std cpcs_pre_std if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_bl = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}      144       145       145
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   DT_score_p~d  rosen_pre_~d  cpcs_pre_std
N {res}          144           145           145
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}       95        98        98
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   DT_score_p~d  rosen_pre_~d  cpcs_pre_std
N {res}           95            98            98
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res}-.0313509  .0382918  .1345164
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      DT_score_p~d  rosen_pre_~d  cpcs_pre_std
Mean {res}   -.03135095     .03829184      .1345164
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .0475214 -.0566567 -.1990291
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      DT_score_p~d  rosen_pre_~d  cpcs_pre_std
Mean {res}    .04752144    -.05665667    -.19902912
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} 1.023177  .9748496  .9271749
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    DT_score_p~d  rosen_pre_~d  cpcs_pre_std
SD {res}    1.0231772     .97484957     .92717486
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} .9672202  1.038561  1.073121
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    DT_score_p~d  rosen_pre_~d  cpcs_pre_std
SD {res}    .96722024      1.038561     1.0731214
{reset}
{com}. 
. matrix n_bl = J(1,3,.)
{txt}
{com}. forvalues i = 1/3 {c -(}
{txt}  2{com}.         matrix n_bl[1,`i'] = n_tr_bl[1,`i'] + n_ct_bl[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in DT_score_pre_std rosen_pre_std cpcs_pre_std{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}239
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 32
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  2
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.5
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        DT_score_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0788724{col 38}{space 1}  -0.38{col 46}{space 3}0.726{col 54}{space 3}-.5170202{col 66}{space 3} .3849625
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}           rosen_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0949485{col 38}{space 1}   0.47{col 46}{space 3}0.600{col 54}{space 3}-.3528281{col 66}{space 3} .5243389
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}            cpcs_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3335455{col 38}{space 1}   1.82{col 46}{space 3}0.116{col 54}{space 3}-.0763347{col 66}{space 3} .7082129
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/mb/s9qljd594gbfyhl2dqnnwlcw0000gn/T//SD56186.000000"
{txt}
{com}. version 18.5
{txt}
{com}. clear all
{res}{txt}
{com}. set more off
{txt}
{com}. 
. 
. * set the path to global
. 
. global path_replication "/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package"
{txt}
{com}. 
. global path_output "$path_replication/outputs"
{txt}
{com}. 
. global path_data "$path_replication/data"
{txt}
{com}. 
. global path_do "$path_replication/do_files"
{txt}
{com}. 
. adopath + "$path_replication/ado"
{txt}  [1]  (BASE)      "{res}/Applications/Stata/ado/base/{txt}"
  [2]  (SITE)      "{res}/Applications/Stata/ado/site/{txt}"
  [3]              "{res}.{txt}"
  [4]  (PERSONAL)  "{res}/Users/takakurakazuma/Documents/Stata/ado/personal/{txt}"
  [5]  (PLUS)      "{res}/Users/takakurakazuma/Library/Application Support/Stata/ado/plus/{txt}"
  [6]  (OLDPLACE)  "{res}~/ado/{txt}"
  [7]              "{res}CHANGE TO YOUR PATH/ado{txt}"
  [8]              "{res}/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/ado{txt}"

{com}. 
. 
. 
. * log using "$path_output/log_all", replace
. 
. set seed 123
{txt}
{com}. 
. **************************************************
. 
. 
. *** run the code for cleaning.
. 
. do "$path_do/1_data_cleaning_students.do"
{txt}
{com}. clear all
{res}{txt}
{com}. set more off
{txt}
{com}. 
. 
. /// prepare baseline teacher information
> use "$path_data/original_teacher.dta", clear
{txt}
{com}. drop if endline == 1
{txt}(1,004 observations deleted)

{com}. keep student_no age_tchr gender_tchr edu_tchr
{txt}
{com}. sort student_no
{txt}
{com}. save "$path_data/temp/teacher", replace
{txt}{p 0 4 2}
file {bf}
/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/data/temp/teacher.dta{rm}
saved
{p_end}

{com}. 
. import excel "$path_data/followup_students_master.xlsx", clear first
{res}{text}(255 vars, 287 obs)

{com}. gen student_no = q1b
{txt}
{com}. 
. /// remove imcomplete interview
> drop if q0a == "319003"
{txt}(1 observation deleted)

{com}. 
. /// Yes==1, No==0, Dont know ==.
> recode q2a q2c q2h q3a q3e q4a q7a q7b q9a1 q9a3 q9b1 (2=0)
{txt}(127 changes made to {bf:q2a})
(268 changes made to {bf:q2c})
(173 changes made to {bf:q2h})
(170 changes made to {bf:q3a})
(41 changes made to {bf:q3e})
(24 changes made to {bf:q4a})
(56 changes made to {bf:q7a})
(11 changes made to {bf:q7b})
(24 changes made to {bf:q9a1})
(267 changes made to {bf:q9a3})
(0 changes made to {bf:q9b1})

{com}. recode q9b1 (3=.)
{txt}(2 changes made to {bf:q9b1})

{com}. 
. 
. 
. 
. // other changes
. 
. gen PSC_grade = q2k2
{txt}(62 missing values generated)

{com}. replace PSC_grade ="0" if q2k2== "Auto"
{txt}(5 real changes made)

{com}. replace PSC_grade ="0" if q2k2== "mone nai"
{txt}(1 real change made)

{com}. replace PSC_grade ="0" if q2k2== ""
{txt}(62 real changes made)

{com}. replace PSC_grade ="3.08" if q2k2=="3.o8"
{txt}(1 real change made)

{com}. destring PSC_grade, replace
{txt}PSC_grade: all characters numeric; {res}replaced {txt}as {res}double
{txt}
{com}. recode PSC_grade(0=.)
{txt}(68 changes made to {bf:PSC_grade})

{com}. 
. gen JSC_grade = q2l2
{txt}(146 missing values generated)

{com}. gen JSC_auto = 0
{txt}
{com}. replace JSC_auto = 1 if q2l2 == "Ato pas"
{txt}(3 real changes made)

{com}. replace JSC_auto = 1 if q2l2 == "Atou pass"
{txt}(1 real change made)

{com}. replace JSC_auto = 1 if q2l2 == "Auto"
{txt}(32 real changes made)

{com}. replace JSC_auto = 1 if q2l2 == "Auto  pass"
{txt}(4 real changes made)

{com}. replace JSC_auto = 1 if q2l2 == "Auto Pass"
{txt}(8 real changes made)

{com}. replace JSC_auto = 1 if q2l2 == "Auto pass"
{txt}(22 real changes made)

{com}. replace JSC_auto = 1 if q2l2 == "Autopash."
{txt}(1 real change made)

{com}. replace JSC_auto = 1 if q2l2 == "Autopass"
{txt}(1 real change made)

{com}. replace JSC_auto = 1 if q2l2 == "auto pass"
{txt}(11 real changes made)

{com}. replace JSC_auto = 1 if q2l2 == "result school thake deyni"
{txt}(1 real change made)

{com}. replace JSC_grade = "0" if JSC_auto == 1
{txt}(84 real changes made)

{com}. replace JSC_grade = "0" if q2l2 == ""
{txt}(146 real changes made)

{com}. destring JSC_grade, replace
{txt}JSC_grade: all characters numeric; {res}replaced {txt}as {res}double
{txt}
{com}. recode JSC_grade(0=.)
{txt}(230 changes made to {bf:JSC_grade})

{com}. 
. 
. local q5an 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
{txt}
{com}. foreach i in `q5an'{c -(}
{txt}  2{com}. gen q5a_`i' = 1 if q5a==`i'
{txt}  3{com}. recode q5a_`i'(.=0)
{txt}  4{com}. {c )-}
{txt}(285 missing values generated)
(285 changes made to {bf:q5a_1})
(270 missing values generated)
(270 changes made to {bf:q5a_2})
(265 missing values generated)
(265 changes made to {bf:q5a_3})
(188 missing values generated)
(188 changes made to {bf:q5a_4})
(264 missing values generated)
(264 changes made to {bf:q5a_5})
(284 missing values generated)
(284 changes made to {bf:q5a_6})
(282 missing values generated)
(282 changes made to {bf:q5a_7})
(263 missing values generated)
(263 changes made to {bf:q5a_8})
(285 missing values generated)
(285 changes made to {bf:q5a_9})
(286 missing values generated)
(286 changes made to {bf:q5a_10})
(286 missing values generated)
(286 changes made to {bf:q5a_11})
(217 missing values generated)
(217 changes made to {bf:q5a_12})
(279 missing values generated)
(279 changes made to {bf:q5a_13})
(286 missing values generated)
(286 changes made to {bf:q5a_14})
(284 missing values generated)
(284 changes made to {bf:q5a_15})
(286 missing values generated)
(286 changes made to {bf:q5a_16})

{com}. 
. local q5bn 1 2 3 4 5 6 7 8 9
{txt}
{com}. foreach i in `q5bn'{c -(}
{txt}  2{com}. gen q5b_`i' = 1 if q5b==`i'
{txt}  3{com}. recode q5b_`i'(.=0)
{txt}  4{com}. {c )-}
{txt}(243 missing values generated)
(243 changes made to {bf:q5b_1})
(189 missing values generated)
(189 changes made to {bf:q5b_2})
(260 missing values generated)
(260 changes made to {bf:q5b_3})
(281 missing values generated)
(281 changes made to {bf:q5b_4})
(231 missing values generated)
(231 changes made to {bf:q5b_5})
(285 missing values generated)
(285 changes made to {bf:q5b_6})
(284 missing values generated)
(284 changes made to {bf:q5b_7})
(277 missing values generated)
(277 changes made to {bf:q5b_8})
(283 missing values generated)
(283 changes made to {bf:q5b_9})

{com}. 
. 
. 
. gen q6a1_correct = 1 if q6a1==10800
{txt}(88 missing values generated)

{com}. gen q6a2_correct = 1 if q6a2==9
{txt}(43 missing values generated)

{com}. gen q6a3a_correct = 1 if q6a3a==70
{txt}(70 missing values generated)

{com}. gen q6a3b_correct = 1 if q6a3b==50
{txt}(46 missing values generated)

{com}. gen q6a4_correct = 1 if q6a4==20
{txt}(211 missing values generated)

{com}. gen q6a5_correct = 1 if q6a5==5
{txt}(224 missing values generated)

{com}. 
. recode q6a1_correct q6a2_correct q6a3a_correct q6a3b_correct q6a4_correct q6a5_correct (.=0)
{txt}(88 changes made to {bf:q6a1_correct})
(43 changes made to {bf:q6a2_correct})
(70 changes made to {bf:q6a3a_correct})
(46 changes made to {bf:q6a3b_correct})
(211 changes made to {bf:q6a4_correct})
(224 changes made to {bf:q6a5_correct})

{com}. 
. save "$path_data/temp/followup_student_data", replace
{txt}{p 0 4 2}
file {bf}
/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/data/temp/followup_student_data.dta{rm}
saved
{p_end}

{com}.  
. 
. import excel "$path_data/followup_students_extra.xlsx",  clear first
{res}{text}(50 vars, 222 obs)

{com}. drop if q1b==1223 & _index==64
{txt}(1 observation deleted)

{com}. drop if q1b==2804 & _index==116
{txt}(1 observation deleted)

{com}. keep q1b q3c1new q3c2new q3e _index
{txt}
{com}. rename q3e q3enew
{res}{txt}
{com}. recode q3enew(2=0)
{txt}(155 changes made to {bf:q3enew})

{com}. destring q1b, replace
{txt}q1b already numeric; no {res}replace
{txt}
{com}. merge 1:1 q1b using "$path_data/temp/followup_student_data"
{res}
{txt}{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}              68
{txt}{col 9}from master{col 30}{res}               1{txt}  (_merge==1)
{col 9}from using{col 30}{res}              67{txt}  (_merge==2)

{col 5}Matched{col 30}{res}             219{txt}  (_merge==3)
{col 5}{hline 41}

{com}. save "$path_data/temp/followup_student_data", replace
{txt}{p 0 4 2}
file {bf}
/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/data/temp/followup_student_data.dta{rm}
saved
{p_end}

{com}. 
. 
. // check the accuracy of q3c
. drop if _merge==1
{txt}(1 observation deleted)

{com}. drop _merge
{txt}
{com}. sum q3c1new

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}q3c1new {c |}{res}         94    5.851064    .6038843          4          7
{txt}
{com}. sum q3c2new

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}q3c2new {c |}{res}         94    9.829787    3.999028          4         21
{txt}
{com}. * tab treatment q3enew
. tab q3e q3enew

           {txt}{c |}          q3e
       q3e {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}        14         11 {txt}{c |}{res}        25 
{txt}         1 {c |}{res}       141         53 {txt}{c |}{res}       194 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       155         64 {txt}{c |}{res}       219 
{txt}
{com}. 
. // merge with baseline & endline
. use "$path_data/original_raw_score", clear
{txt}
{com}. keep student_no DT_score_pre cpcs_pre rosen_pre
{txt}
{com}. save "$path_data/temp/rawscore", replace
{txt}{p 0 4 2}
file {bf}
/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/data/temp/rawscore.dta{rm}
saved
{p_end}

{com}. 
. use "$path_data/temp/followup_student_data", clear
{txt}
{com}. drop _merge 
{txt}
{com}. merge 1:1 student_no using "$path_data/original_main"
{res}{txt}(label {bf:{txt}_merge} already defined)

{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}             812
{txt}{col 9}from master{col 30}{res}              44{txt}  (_merge==1)
{col 9}from using{col 30}{res}             768{txt}  (_merge==2)

{col 5}Matched{col 30}{res}             243{txt}  (_merge==3)
{col 5}{hline 41}

{com}. rename _merge _merge_base_character
{res}{txt}
{com}. merge 1:1 student_no using "$path_data/temp/rawscore"
{res}
{txt}{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}              44
{txt}{col 9}from master{col 30}{res}              44{txt}  (_merge==1)
{col 9}from using{col 30}{res}               0{txt}  (_merge==2)

{col 5}Matched{col 30}{res}           1,011{txt}  (_merge==3)
{col 5}{hline 41}

{com}. rename _merge _merge_base_score
{res}{txt}
{com}. tab treatment _merge_base_score

           {txt}{c |}  Matching
           {c |}   result
           {c |} from merge
 treatment {c |} Matched ( {c |}     Total
{hline 11}{c +}{hline 11}{c +}{hline 10}
         0 {c |}{res}       478 {txt}{c |}{res}       478 
{txt}         1 {c |}{res}       526 {txt}{c |}{res}       526 
{txt}{hline 11}{c +}{hline 11}{c +}{hline 10}
     Total {c |}{res}     1,004 {txt}{c |}{res}     1,004 
{txt}
{com}. 
. gen attrition = 0 if _merge_base_character == 3
{txt}(812 missing values generated)

{com}. recode attrition (.=1)
{txt}(812 changes made to {bf:attrition})

{com}. tab attrition

  {txt}attrition {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        243       23.03       23.03
{txt}          1 {c |}{res}        812       76.97      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,055      100.00
{txt}
{com}. 
. // fix missing values using baseline information
. replace school_no = 18 if school_no == . & student_no == 1817
{txt}(1 real change made)

{com}. replace treatment = 1 if student_no == 1817
{txt}(1 real change made)

{com}. replace grade = 2 if student_no == 1817
{txt}(1 real change made)

{com}. replace branch1 = 0 if student_no == 1817
{txt}(1 real change made)

{com}. replace branch2 = 0 if student_no == 1817
{txt}(1 real change made)

{com}. replace branch3 = 1 if student_no == 1817
{txt}(1 real change made)

{com}. replace branch4 = 0 if student_no == 1817
{txt}(1 real change made)

{com}. 
. save "$path_data/temp/student_unbalance", replace
{txt}{p 0 4 2}
file {bf}
/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/data/temp/student_unbalance.dta{rm}
saved
{p_end}

{com}. 
. 
. // keep balanced panel
. keep if attrition == 0
{txt}(812 observations deleted)

{com}. 
. save "$path_data/temp/endline_followup_student_data", replace
{txt}{p 0 4 2}
file {bf}
/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/data/temp/endline_followup_student_data.dta{rm}
saved
{p_end}

{com}. 
. 
. gen followup_cog = q6a1_correct + q6a2_correct + q6a3a_correct + q6a3b_correct + q6a4_correct + q6a5_correct
{txt}
{com}. 
. /// non-cog
> // positive: 2,3,5,7,10,11,12,17,18,20,21,22,23,25,26,27,28,29,32,33,34,36,37,39
. // positive-cog:1,13,14,19,24,
. // negative: 4,6,8,9,30,31,35,38,40
. 
. local q99 q6c1 q6c2 q6c3 q6c4 q6c5 q6c6 q6c7 q6c8 q6c9 q6c10 q6c11 q6c12 q6c13 q6c14 q6c15 q6c16 q6c17 q6c18 q6c19 q6c20 ///
> q6c21 q6c22 q6c23 q6c24 q6c25 q6c26 q6c27 q6c28 q6c29 q6c30 q6c31 q6c32 q6c33 q6c34 q6c35 q6c36 q6c37 q6c38 q6c39 q6c40 ///
> q8a1a q8a2a q8a3a q8a4a q8a5a
{txt}
{com}. 
. foreach y in `q99'{c -(}
{txt}  2{com}. replace `y'=.  if `y'==99
{txt}  3{com}. {c )-}
{txt}(1 real change made, 1 to missing)
(1 real change made, 1 to missing)
(1 real change made, 1 to missing)
(0 real changes made)
(3 real changes made, 3 to missing)
(2 real changes made, 2 to missing)
(0 real changes made)
(2 real changes made, 2 to missing)
(0 real changes made)
(0 real changes made)
(1 real change made, 1 to missing)
(1 real change made, 1 to missing)
(0 real changes made)
(0 real changes made)
(6 real changes made, 6 to missing)
(8 real changes made, 8 to missing)
(1 real change made, 1 to missing)
(12 real changes made, 12 to missing)
(12 real changes made, 12 to missing)
(5 real changes made, 5 to missing)
(1 real change made, 1 to missing)
(2 real changes made, 2 to missing)
(2 real changes made, 2 to missing)
(7 real changes made, 7 to missing)
(13 real changes made, 13 to missing)
(1 real change made, 1 to missing)
(2 real changes made, 2 to missing)
(1 real change made, 1 to missing)
(8 real changes made, 8 to missing)
(1 real change made, 1 to missing)
(0 real changes made)
(1 real change made, 1 to missing)
(0 real changes made)
(0 real changes made)
(26 real changes made, 26 to missing)
(16 real changes made, 16 to missing)
(0 real changes made)
(0 real changes made)
(1 real change made, 1 to missing)
(20 real changes made, 20 to missing)
(3 real changes made, 3 to missing)
(2 real changes made, 2 to missing)
(4 real changes made, 4 to missing)
(0 real changes made)
(1 real change made, 1 to missing)

{com}. 
. gen noncog4 = 5 - q6c4
{txt}
{com}. gen noncog6 = 5 - q6c6
{txt}(2 missing values generated)

{com}. gen noncog8 = 5 - q6c8
{txt}(2 missing values generated)

{com}. gen noncog9 = 5 - q6c9
{txt}
{com}. gen noncog30 = 5 - q6c30
{txt}(1 missing value generated)

{com}. gen noncog31 = 5 - q6c31
{txt}
{com}. gen noncog35 = 5 - q6c35
{txt}(26 missing values generated)

{com}. gen noncog38 = 5 - q6c38
{txt}
{com}. gen noncog40 = 5 - q6c40
{txt}(20 missing values generated)

{com}. 
. gen followup_noncog = q6c1+q6c2+q6c3+noncog4+q6c5+noncog6+q6c7+noncog8+noncog9+q6c10+q6c11+q6c12+q6c13+q6c14+q6c17+q6c18+q6c19+q6c20+q6c21+q6c22+q6c23+q6c24+q6c25+q6c26+q6c27+q6c28+q6c29+noncog30+noncog31+q6c32+q6c33+q6c34+noncog35+q6c36+q6c37+noncog38+noncog40+q6c39
{txt}(64 missing values generated)

{com}. gen followup_noncog2 = q6c2+q6c3+noncog4+q6c5+noncog6+q6c7+noncog8+noncog9+q6c10+q6c11+q6c12+q6c17+q6c18+q6c20+q6c21+q6c22+q6c23+q6c25+q6c26+q6c27+q6c28+q6c29+noncog30+noncog31+q6c32+q6c33+q6c34+noncog35+q6c36+q6c37+noncog38+noncog40+q6c39
{txt}(63 missing values generated)

{com}. 
. sum followup_noncog followup_noncog2

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
followup_n~g {c |}{res}        179    64.49721    16.11419         31        111
{txt}followup_n~2 {c |}{res}        180    55.78889    14.00558         25         94
{txt}
{com}. 
. replace followup_noncog = 190 - followup_noncog
{txt}(179 real changes made)

{com}. replace followup_noncog2 = 180 - followup_noncog2
{txt}(179 real changes made)

{com}. 
. gen RSES = 40 - q6c2 - q6c3 - noncog4 - noncog6 - noncog8 - noncog9 - q6c10 - q6c11
{txt}(7 missing values generated)

{com}. gen CPCS = 50 - q6c2 - q6c3 - noncog4 - q6c5 - noncog6 -q6c7 - noncog8 - noncog9 - q6c10 - q6c11
{txt}(7 missing values generated)

{com}. 
. /// variables for study situation
> gen tutor = 1 if q3a == 1
{txt}(149 missing values generated)

{com}. gen study_other = 1 if q4a == 1
{txt}(134 missing values generated)

{com}. gen study_affect_covid = 1 if q9a21 == 1
{txt}(90 missing values generated)

{com}. gen hometutoring = 1 if q9a2a1 == 1
{txt}(215 missing values generated)

{com}. gen onlineclass = 1 if q9a2a2 == 1
{txt}(223 missing values generated)

{com}. gen studymyself = 1 if q9a2a3 == 1
{txt}(115 missing values generated)

{com}. gen parentsteach = 1 if q9a2a4 == 1
{txt}(220 missing values generated)

{com}. recode tutor study_other study_affect_covid hometutoring onlineclass studymyself parentsteach (.=0)
{txt}(149 changes made to {bf:tutor})
(134 changes made to {bf:study_other})
(90 changes made to {bf:study_affect_covid})
(215 changes made to {bf:hometutoring})
(223 changes made to {bf:onlineclass})
(115 changes made to {bf:studymyself})
(220 changes made to {bf:parentsteach})

{com}. 
. /// other variable
> gen phone_survey = 1 if q1a0 == 2
{txt}(184 missing values generated)

{com}. recode phone_survey (.=0)
{txt}(184 changes made to {bf:phone_survey})

{com}. 
. /// Standardization
> egen DT_score_pre_mean = mean(DT_score_pre)
{txt}
{com}. egen DT_score_pre_sd = sd(DT_score_pre)
{txt}
{com}. gen DT_score_pre_std = (DT_score_pre-DT_score_pre_mean)/DT_score_pre_sd
{txt}(4 missing values generated)

{com}. drop DT_score_pre_mean DT_score_pre_sd 
{txt}
{com}. 
. egen cpcs_pre_mean = mean(cpcs_pre)
{txt}
{com}. egen cpcs_pre_sd = sd(cpcs_pre)
{txt}
{com}. gen cpcs_pre_std = (cpcs_pre-cpcs_pre_mean)/cpcs_pre_sd
{txt}
{com}. drop cpcs_pre_mean cpcs_pre_sd 
{txt}
{com}. 
. egen rosen_pre_mean = mean(rosen_pre)
{txt}
{com}. egen rosen_pre_sd = sd(rosen_pre)
{txt}
{com}. gen rosen_pre_std = (rosen_pre-rosen_pre_mean)/rosen_pre_sd
{txt}
{com}. drop rosen_pre_mean rosen_pre_sd 
{txt}
{com}. 
. egen followup_cog_mean = mean(followup_cog)
{txt}
{com}. egen followup_cog_sd = sd(followup_cog)
{txt}
{com}. gen followup_cog_std = (followup_cog-followup_cog_mean)/followup_cog_sd
{txt}
{com}. drop followup_cog_mean followup_cog_sd 
{txt}
{com}. 
. egen followup_noncog_mean = mean(followup_noncog)
{txt}
{com}. egen followup_noncog_sd = sd(followup_noncog)
{txt}
{com}. gen followup_noncog_std = (followup_noncog - followup_noncog_mean)/followup_noncog_sd
{txt}(64 missing values generated)

{com}. drop followup_noncog_mean followup_noncog_sd 
{txt}
{com}. 
. egen CPCS_mean = mean(CPCS)
{txt}
{com}. egen CPCS_sd = sd(CPCS)
{txt}
{com}. gen CPCS_std = (CPCS - CPCS_mean)/CPCS_sd
{txt}(7 missing values generated)

{com}. drop CPCS_mean CPCS_sd 
{txt}
{com}. 
. egen RSES_mean = mean(RSES)
{txt}
{com}. egen RSES_sd = sd(RSES)
{txt}
{com}. gen RSES_std = (RSES-RSES_mean)/RSES_sd
{txt}(7 missing values generated)

{com}. drop RSES_mean RSES_sd 
{txt}
{com}. 
. /// missing
> gen DT_score_pre_std_missing_dummy = 1 if DT_score_pre_std == .
{txt}(239 missing values generated)

{com}. gen cpcs_pre_std_missing_dummy = 1 if cpcs_pre_std == .
{txt}(243 missing values generated)

{com}. gen rosen_pre_std_missing_dummy = 1 if rosen_pre_std == .
{txt}(243 missing values generated)

{com}. recode DT_score_pre_std_missing_dummy cpcs_pre_std_missing_dummy rosen_pre_std_missing_dummy (.=0)
{txt}(239 changes made to {bf:DT_score_pre_std_missing_dummy})
(243 changes made to {bf:cpcs_pre_std_missing_dummy})
(243 changes made to {bf:rosen_pre_std_missing_dummy})

{com}. 
. gen DT_score_pre_std_missing_0 = DT_score_pre_std if DT_score_pre_std_missing == 0
{txt}(4 missing values generated)

{com}. gen cpcs_pre_std_missing_0 = cpcs_pre_std if cpcs_pre_std != .
{txt}
{com}. gen rosen_pre_std_missing_0 = rosen_pre_std if rosen_pre_std != .
{txt}
{com}. recode DT_score_pre_std_missing_0 cpcs_pre_std_missing_0 rosen_pre_std_missing_0 (.=0)
{txt}(4 changes made to {bf:DT_score_pre_std_missing_0})
(0 changes made to {bf:cpcs_pre_std_missing_0})
(0 changes made to {bf:rosen_pre_std_missing_0})

{com}. 
. gen hyper = 1 if q7d2a == 1 & q7d2b == 2
{txt}(231 missing values generated)

{com}. replace hyper = 1 if q7d2a == 1 & q7d2b == 3
{txt}(10 real changes made)

{com}. replace hyper = 1 if q7d2a == 2 & q7d2b == 3
{txt}(13 real changes made)

{com}. gen hypernoinfo = 1 if q7d2a == .
{txt}(184 missing values generated)

{com}. recode hyper hypernoinfo (.=0)
{txt}(208 changes made to {bf:hyper})
(184 changes made to {bf:hypernoinfo})

{com}. replace hyper = . if hypernoinfo == 1
{txt}(59 real changes made, 59 to missing)

{com}. 
. 
. 
. /// merge teacher information
> merge 1:1 student_no using "$path_data/temp/teacher"
{res}
{txt}{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}             763
{txt}{col 9}from master{col 30}{res}               1{txt}  (_merge==1)
{col 9}from using{col 30}{res}             762{txt}  (_merge==2)

{col 5}Matched{col 30}{res}             242{txt}  (_merge==3)
{col 5}{hline 41}

{com}. rename _merge _merge_teacher
{res}{txt}
{com}. recode age_tchr(.=0)
{txt}(33 changes made to {bf:age_tchr})

{com}. gen age_tchr_missing_dummy = 1 if age_tchr == 0
{txt}(972 missing values generated)

{com}. recode age_tchr_missing_dummy(.=0)
{txt}(972 changes made to {bf:age_tchr_missing_dummy})

{com}. 
. 
. save "$path_data/temp/followup_student_baseline_score_missing_dummy", replace
{txt}{p 0 4 2}
file {bf}
/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/data/temp/followup_student_baseline_score_missing_dummy.dta{rm}
saved
{p_end}

{com}. 
. 
. 
. 
{txt}end of do-file

{com}. 
. do "$path_do/1_data_cleaning_parents.do"
{txt}
{com}. clear all
{res}{txt}
{com}. set more off
{txt}
{com}. 
. import excel "$path_data/followup_parents_master.xlsx", clear first
{res}{text}(593 vars, 230 obs)

{com}. 
. local q3888999 b3 b7 ///
> cm1_2 cm1_3 cm1_4 cm1_5 cm1_6 cm1_7 cm1_8 cm1_9 cm1_10 cm1_11 ///
> cm2_2 cm2_3 cm2_4 cm2_5 cm2_6 cm2_7 cm2_8 cm2_9 cm2_10 cm2_11 ///
> cm3_2 cm3_3 cm3_4 cm3_5 cm3_6 cm3_7 cm3_8 cm3_9 cm3_10 cm3_11 ///
> cm4_2 cm4_3 cm4_4 cm4_5 cm4_6 cm4_7 cm4_8 cm4_9 cm4_10 cm4_11 ///
> e2 f2_1
{txt}
{com}. 
. local yesno f1_1 f1_3 f2_1
{txt}
{com}. 
. local missingzero e9_1 e9_2 e9_3 e9_4 e9_5 e9_6 e9_7 e9_8 e9_9
{txt}
{com}. 
. foreach y in `q3888999'{c -(}
{txt}  2{com}. replace `y'=.  if `y'==3
{txt}  3{com}. replace `y'=.  if `y'==888
{txt}  4{com}. replace `y'=.  if `y'==999
{txt}  5{com}. {c )-}
{txt}(21 real changes made, 21 to missing)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(1 real change made, 1 to missing)
(3 real changes made, 3 to missing)
(0 real changes made)
(0 real changes made)
(5 real changes made, 5 to missing)
(0 real changes made)
(0 real changes made)
(5 real changes made, 5 to missing)
(0 real changes made)
(3 real changes made, 3 to missing)
(2 real changes made, 2 to missing)
(0 real changes made)
(1 real change made, 1 to missing)
(4 real changes made, 4 to missing)
(0 real changes made)
(4 real changes made, 4 to missing)
(1 real change made, 1 to missing)
(0 real changes made)
(2 real changes made, 2 to missing)
(2 real changes made, 2 to missing)
(0 real changes made)
(2 real changes made, 2 to missing)
(4 real changes made, 4 to missing)
(0 real changes made)
(8 real changes made, 8 to missing)
(5 real changes made, 5 to missing)
(0 real changes made)
(3 real changes made, 3 to missing)
(2 real changes made, 2 to missing)
(0 real changes made)
(0 real changes made)
(1 real change made, 1 to missing)
(0 real changes made)
(1 real change made, 1 to missing)
(3 real changes made, 3 to missing)
(0 real changes made)
(1 real change made, 1 to missing)
(2 real changes made, 2 to missing)
(0 real changes made)
(1 real change made, 1 to missing)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(3 real changes made, 3 to missing)
(0 real changes made)
(2 real changes made, 2 to missing)
(3 real changes made, 3 to missing)
(0 real changes made)
(1 real change made, 1 to missing)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(2 real changes made, 2 to missing)
(0 real changes made)
(4 real changes made, 4 to missing)
(4 real changes made, 4 to missing)
(0 real changes made)
(1 real change made, 1 to missing)
(4 real changes made, 4 to missing)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(1 real change made, 1 to missing)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(1 real change made, 1 to missing)
(1 real change made, 1 to missing)
(2 real changes made, 2 to missing)
(1 real change made, 1 to missing)
(1 real change made, 1 to missing)
(0 real changes made)
(1 real change made, 1 to missing)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(3 real changes made, 3 to missing)
(0 real changes made)
(0 real changes made)
(2 real changes made, 2 to missing)
(0 real changes made)
(2 real changes made, 2 to missing)
(0 real changes made)
(1 real change made, 1 to missing)
(1 real change made, 1 to missing)
(1 real change made, 1 to missing)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(119 real changes made, 119 to missing)
(1 real change made, 1 to missing)
(0 real changes made)
(3 real changes made, 3 to missing)
(0 real changes made)
(0 real changes made)

{com}. 
. foreach y in `yesno'{c -(}
{txt}  2{com}. replace `y'=0  if `y'==2
{txt}  3{com}. {c )-}
{txt}(1 real change made)
(212 real changes made)
(2 real changes made)

{com}. 
. foreach y in `missingzero'{c -(}
{txt}  2{com}. replace `y'=0  if `y'==.
{txt}  3{com}. {c )-}
{txt}(55 real changes made)
(109 real changes made)
(230 real changes made)
(22 real changes made)
(195 real changes made)
(227 real changes made)
(221 real changes made)
(204 real changes made)
(9 real changes made)

{com}. 
. gen hhmember = a3
{txt}
{com}. gen hhheadage = am1_3a if am1_4 == 1
{txt}(2 missing values generated)

{com}. replace hhheadage = am2_3a if am2_4 == 1
{txt}(0 real changes made)

{com}. replace hhheadage = am3_3a if am3_4 == 1
{txt}(2 real changes made)

{com}. replace hhheadage = am4_3a if am4_4 == 1
{txt}(0 real changes made)

{com}. gen hhheadedu = am1_6 if am1_4 == 1
{txt}(2 missing values generated)

{com}. replace hhheadedu = am2_6 if am2_4 == 1
{txt}(0 real changes made)

{com}. replace hhheadedu = am3_6 if am3_4 == 1
{txt}(1 real change made)

{com}. replace hhheadedu = am4_6 if am4_4 == 1
{txt}(0 real changes made)

{com}. 
. gen hhheadeduyear = hhheadedu
{txt}(1 missing value generated)

{com}. replace hhheadeduyear = 10 if hhheadedu == 11
{txt}(1 real change made)

{com}. replace hhheadeduyear = 0 if hhheadedu == 17
{txt}(51 real changes made)

{com}. replace hhheadeduyear = 18 if hhheadedu == 15
{txt}(1 real change made)

{com}. replace hhheadeduyear = . if hhheadedu == 888
{txt}(2 real changes made, 2 to missing)

{com}. replace hhheadeduyear = . if hhheadedu == 999
{txt}(1 real change made, 1 to missing)

{com}. 
. destring x1d x1f x1h , replace
{txt}x1d: all characters numeric; {res}replaced {txt}as {res}int
{txt}x1f: all characters numeric; {res}replaced {txt}as {res}long
{txt}(167 missing values generated)
{res}{txt}x1h: all characters numeric; {res}replaced {txt}as {res}int
{txt}(228 missing values generated)
{res}{txt}
{com}. 
. keep x1d x1f x1h hhmember hhheadage hhheadedu hhheadeduyear
{txt}
{com}. 
. preserve
{txt}
{com}. collapse (mean) hhmember hhheadage hhheadedu hhheadeduyear, by(x1d)
{res}{txt}
{com}. replace hhheadeduyear = . if hhheadeduyear == 4.5
{txt}(1 real change made, 1 to missing)

{com}. rename x1d student_no
{res}{txt}
{com}. save "$path_data/temp/endline_followup_parents_data_1stchild", replace
{txt}{p 0 4 2}
file {bf}
/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/data/temp/endline_followup_parents_data_1stchild.dta{rm}
saved
{p_end}

{com}. 
. restore
{txt}
{com}. preserve
{txt}
{com}. rename x1f student_no
{res}{txt}
{com}. drop if student_no == .
{txt}(167 observations deleted)

{com}. collapse (mean) hhmember hhheadage hhheadedu hhheadeduyear, by(student_no)
{res}{txt}
{com}. replace hhheadeduyear = . if hhheadeduyear == 4.5
{txt}(1 real change made, 1 to missing)

{com}. save "$path_data/temp/endline_followup_parents_data_2ndchild", replace
{txt}{p 0 4 2}
file {bf}
/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/data/temp/endline_followup_parents_data_2ndchild.dta{rm}
saved
{p_end}

{com}. 
. restore
{txt}
{com}. preserve
{txt}
{com}. rename x1h student_no
{res}{txt}
{com}. drop if student_no == .
{txt}(228 observations deleted)

{com}. collapse (mean) hhmember hhheadage hhheadedu hhheadeduyear, by(student_no)
{res}{txt}
{com}. replace hhheadeduyear = . if hhheadeduyear == 4.5
{txt}(0 real changes made)

{com}. save "$path_data/temp/endline_followup_parents_data_3rdchild", replace
{txt}{p 0 4 2}
file {bf}
/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/data/temp/endline_followup_parents_data_3rdchild.dta{rm}
saved
{p_end}

{com}. 
. 
. 
{txt}end of do-file

{com}. 
. do "$path_do/1_data_cleaning_merge.do"
{txt}
{com}. set more off
{txt}
{com}. clear all
{res}{txt}
{com}. 
. use "$path_data/temp/followup_student_baseline_score_missing_dummy", clear
{txt}
{com}. merge 1:1 student_no using "$path_data/temp/endline_followup_parents_data_1stchild"
{res}
{txt}{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}             789
{txt}{col 9}from master{col 30}{res}             783{txt}  (_merge==1)
{col 9}from using{col 30}{res}               6{txt}  (_merge==2)

{col 5}Matched{col 30}{res}             222{txt}  (_merge==3)
{col 5}{hline 41}

{com}. rename _merge _merge_1st
{res}{txt}
{com}. rename hhmember hhmember_1st
{res}{txt}
{com}. rename hhheadage hhheadage_1st
{res}{txt}
{com}. rename hhheadeduyear hhheadeduyear_1st
{res}{txt}
{com}. 
. merge 1:1 student_no using "$path_data/temp/endline_followup_parents_data_2ndchild"
{res}{txt}{p 0 7 2}
(variable
{bf:student_no} was {bf:float}, now {bf:double} to accommodate using data's values)
{p_end}

{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}           1,025
{txt}{col 9}from master{col 30}{res}             987{txt}  (_merge==1)
{col 9}from using{col 30}{res}              38{txt}  (_merge==2)

{col 5}Matched{col 30}{res}              24{txt}  (_merge==3)
{col 5}{hline 41}

{com}. rename _merge _merge_2nd
{res}{txt}
{com}. rename hhmember hhmember_2nd
{res}{txt}
{com}. rename hhheadage hhheadage_2nd
{res}{txt}
{com}. rename hhheadeduyear hhheadeduyear_2nd
{res}{txt}
{com}. 
. merge 1:1 student_no using "$path_data/temp/endline_followup_parents_data_3rdchild"
{res}
{txt}{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}           1,047
{txt}{col 9}from master{col 30}{res}           1,047{txt}  (_merge==1)
{col 9}from using{col 30}{res}               0{txt}  (_merge==2)

{col 5}Matched{col 30}{res}               2{txt}  (_merge==3)
{col 5}{hline 41}

{com}. rename _merge _merge_3rd
{res}{txt}
{com}. rename hhmember hhmember_3rd
{res}{txt}
{com}. rename hhheadage hhheadage_3rd
{res}{txt}
{com}. rename hhheadeduyear hhheadeduyear_3rd
{res}{txt}
{com}. 
. recode hhmember* hhheadage* hhheadeduyear* (. = 0) 
{txt}(821 changes made to {bf:hhmember_1st})
(987 changes made to {bf:hhmember_2nd})
(1,047 changes made to {bf:hhmember_3rd})
(822 changes made to {bf:hhheadage_1st})
(988 changes made to {bf:hhheadage_2nd})
(1,047 changes made to {bf:hhheadage_3rd})
(826 changes made to {bf:hhheadeduyear_1st})
(991 changes made to {bf:hhheadeduyear_2nd})
(1,047 changes made to {bf:hhheadeduyear_3rd})

{com}. 
. gen hhmember = hhmember_1st + hhmember_2nd + hhmember_3rd
{txt}
{com}. gen hhheadage = hhheadage_1st + hhheadage_2nd + hhheadage_3rd
{txt}
{com}. gen hhheadeduyear = hhheadeduyear_1st + hhheadeduyear_2nd + hhheadeduyear_3rd
{txt}
{com}. 
. keep if attrition == 0
{txt}(806 observations deleted)

{com}. 
. save "$path_data/temp/followup_student_parents_matched", replace
{txt}{p 0 4 2}
file {bf}
/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/data/temp/followup_student_parents_matched.dta{rm}
saved
{p_end}

{com}. 
{txt}end of do-file

{com}. 
. *** run the codes for outputs.
. 
. 
. do "$path_do/2_table_1.do"
{txt}
{com}. * This is the do file to create "Table 1. Summary Statistics"
. set seed 1
{txt}
{com}. 
. use "$path_data/temp/followup_student_parents_matched", clear
{txt}
{com}. 
. corr rosen_pre_std cpcs_pre_std
{txt}(obs=243)

             {c |} rosen_~d cpcs_p~d
{hline 13}{c +}{hline 18}
rosen_pre_~d {c |}{res}   1.0000
{txt}cpcs_pre_std {c |}{res}   0.9026   1.0000

{txt}
{com}. corr RSES_std CPCS_std
{txt}(obs=236)

             {c |} RSES_std CPCS_std
{hline 13}{c +}{hline 18}
    RSES_std {c |}{res}   1.0000
    {txt}CPCS_std {c |}{res}   0.9701   1.0000

{txt}
{com}. 
. 
. /// Varable Selection
> /// Baseline
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat DT_score_pre_std rosen_pre_std cpcs_pre_std if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_bl = r(StatTotal)
{txt}  5{com}. 
. tabstat DT_score_pre_std rosen_pre_std cpcs_pre_std if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_bl = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}      144       145       145
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   DT_score_p~d  rosen_pre_~d  cpcs_pre_std
N {res}          144           145           145
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}       95        98        98
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   DT_score_p~d  rosen_pre_~d  cpcs_pre_std
N {res}           95            98            98
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res}-.0313509  .0382918  .1345164
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      DT_score_p~d  rosen_pre_~d  cpcs_pre_std
Mean {res}   -.03135095     .03829184      .1345164
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .0475214 -.0566567 -.1990291
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      DT_score_p~d  rosen_pre_~d  cpcs_pre_std
Mean {res}    .04752144    -.05665667    -.19902912
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} 1.023177  .9748496  .9271749
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    DT_score_p~d  rosen_pre_~d  cpcs_pre_std
SD {res}    1.0231772     .97484957     .92717486
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} .9672202  1.038561  1.073121
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    DT_score_p~d  rosen_pre_~d  cpcs_pre_std
SD {res}    .96722024      1.038561     1.0731214
{reset}
{com}. 
. matrix n_bl = J(1,3,.)
{txt}
{com}. forvalues i = 1/3 {c -(}
{txt}  2{com}.         matrix n_bl[1,`i'] = n_tr_bl[1,`i'] + n_ct_bl[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in DT_score_pre_std rosen_pre_std cpcs_pre_std{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}239
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 32
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  2
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.5
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        DT_score_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0788724{col 38}{space 1}  -0.38{col 46}{space 3}0.698{col 54}{space 3}-.5187522{col 66}{space 3}  .379723
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}           rosen_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0949485{col 38}{space 1}   0.47{col 46}{space 3}0.646{col 54}{space 3}-.3291105{col 66}{space 3} .5285112
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}            cpcs_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3335455{col 38}{space 1}   1.82{col 46}{space 3}0.074{col 54}{space 3}-.0352127{col 66}{space 3} .7279622
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. /// Family
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat hhmember hhheadage hhheadeduyear if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_parent = r(StatTotal)
{txt}  5{com}. 
. tabstat hhmember hhheadage hhheadeduyear if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_parent = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}      145       145       145
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
       hhmember     hhheadage  hhheadeduy~r
N {res}          145           145           145
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}       98        98        98
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
       hhmember     hhheadage  hhheadeduy~r
N {res}           98            98            98
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res} 4.510345  46.57241  2.331034
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
          hhmember     hhheadage  hhheadeduy~r
Mean {res}    4.5103448     46.572414     2.3310345
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res} 4.265306  46.68878  3.163265
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
          hhmember     hhheadage  hhheadeduy~r
Mean {res}    4.2653061     46.688776     3.1632653
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} 1.280827   9.03907  2.995495
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
        hhmember     hhheadage  hhheadeduy~r
SD {res}    1.2808268     9.0390702     2.9954947
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} 1.197515  9.408681  3.530993
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
        hhmember     hhheadage  hhheadeduy~r
SD {res}    1.1975148     9.4086808     3.5309935
{reset}
{com}. 
. matrix n_parent = J(1,3,.)
{txt}
{com}. forvalues i = 1/3 {c -(}
{txt}  2{com}.         matrix n_parent[1,`i'] = n_tr_parent[1,`i'] + n_ct_parent[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in hhmember hhheadage hhheadeduyear{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                hhmember{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2450387{col 38}{space 1}   1.29{col 46}{space 3}0.202{col 54}{space 3}-.1468311{col 66}{space 3} .6636916
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}               hhheadage{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.1163617{col 38}{space 1}  -0.07{col 46}{space 3}0.964{col 54}{space 3}-3.402025{col 66}{space 3} 3.385659
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:29})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}           hhheadeduyear{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.8322308{col 38}{space 1}  -2.22{col 46}{space 3}0.036{col 54}{space 3}-1.594462{col 66}{space 3}-.0665917
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. 
. 
. /// School　attendance
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat q2a q2b q2c q2h if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_school = r(StatTotal)
{txt}  5{com}. 
. tabstat q2a q2b q2c q2h if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_school = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:N} {...}
{c |}{...}
 {res}      145       145       145       145
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
   q2a  q2b  q2c  q2h
N {res} 145  145  145  145
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:N} {...}
{c |}{...}
 {res}       98        98        98        98
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
   q2a  q2b  q2c  q2h
N {res}  98   98   98   98
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .5517241  9.606897   .062069  .3793103
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
            q2a        q2b        q2c        q2h
Mean {res} .55172414  9.6068966  .06206897  .37931034
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .5306122  9.602041  .0408163  .4489796
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
            q2a        q2b        q2c        q2h
Mean {res} .53061224  9.6020408  .04081633  .44897959
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:SD} {...}
{c |}{...}
 {res} .4990412  1.029405  .2421171  .4868973
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
          q2a        q2b        q2c        q2h
SD {res} .49904123  1.0294048   .2421171  .48689728
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:SD} {...}
{c |}{...}
 {res} .5016279  .8703571  .1988818  .4999474
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
          q2a        q2b        q2c        q2h
SD {res}  .5016279  .87035715  .19888179   .4999474
{reset}
{com}. 
. matrix n_school = J(1,4,.)
{txt}
{com}. forvalues i = 1/4 {c -(}
{txt}  2{com}.         matrix n_school[1,`i'] = n_tr_school[1,`i'] + n_ct_school[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in q2a q2b q2c q2h{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                     q2a{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0211119{col 38}{space 1}   0.25{col 46}{space 3}0.798{col 54}{space 3}-.1531988{col 66}{space 3} .1997139
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                     q2b{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0048557{col 38}{space 1}   0.03{col 46}{space 3}0.950{col 54}{space 3}-.3886809{col 66}{space 3} .3871116
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                     q2c{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0212526{col 38}{space 1}   0.56{col 46}{space 3}0.634{col 54}{space 3}-.0503967{col 66}{space 3} .0975416
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                     q2h{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0696692{col 38}{space 1}  -0.85{col 46}{space 3}0.410{col 54}{space 3}-.2515308{col 66}{space 3} .0998351
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. /// Other study variable
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat tutor study_other study_affect_covid hometutoring onlineclass studymyself parentsteach if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_study = r(StatTotal)
{txt}  5{com}. 
. tabstat tutor study_other study_affect_covid hometutoring onlineclass studymyself parentsteach if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_study = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:N} {...}
{c |}{...}
 {res}      145       145       145       145       145       145       145
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
          tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
N {res}          145           145           145           145           145           145           145
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:N} {...}
{c |}{...}
 {res}       98        98        98        98        98        98        98
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
          tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
N {res}           98            98            98            98            98            98            98
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:Mean} {...}
{c |}{...}
 {res}  .337931   .462069  .6482759  .0965517  .0482759  .5241379  .0275862
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
             tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
Mean {res}    .33793103     .46206897     .64827586     .09655172     .04827586     .52413793     .02758621
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .4591837  .4285714  .6020408  .1428571  .1326531  .5306122  .1938776
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
             tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
Mean {res}    .45918367     .42857143     .60204082     .14285714     .13265306     .53061224     .19387755
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:SD} {...}
{c |}{...}
 {res} .4746445  .5002873  .4791635  .2963701  .2150915  .5011481  .1643517
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
           tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
SD {res}    .47464445     .50028727     .47916354     .29637012     .21509153     .50114811     .16435174
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:SD} {...}
{c |}{...}
 {res} .5008934   .497416  .4919935  .3517262  .3409434  .5016279  .3973667
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
           tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
SD {res}    .50089337       .497416     .49199354     .35172623     .34094336      .5016279     .39736667
{reset}
{com}. 
. matrix n_study = J(1,8,.)
{txt}
{com}. forvalues i = 1/8 {c -(}
{txt}  2{com}.         matrix n_study[1,`i'] = n_tr_study[1,`i'] + n_ct_study[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in tutor study_other study_affect_covid hometutoring onlineclass studymyself parentsteach{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                   tutor{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.1212526{col 38}{space 1}  -1.69{col 46}{space 3}0.140{col 54}{space 3} -.289002{col 66}{space 3} .0428947
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}             study_other{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0334975{col 38}{space 1}   0.39{col 46}{space 3}0.698{col 54}{space 3}-.1530471{col 66}{space 3} .2129988
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}      study_affect_covid{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  .046235{col 38}{space 1}   0.56{col 46}{space 3}0.604{col 54}{space 3}-.1390047{col 66}{space 3} .2363162
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}            hometutoring{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0463054{col 38}{space 1}  -1.11{col 46}{space 3}0.276{col 54}{space 3}-.1304481{col 66}{space 3} .0428661
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:19})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}             onlineclass{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0843772{col 38}{space 1}  -1.92{col 46}{space 3}0.102{col 54}{space 3}-.1754451{col 66}{space 3} .0172149
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:29})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}             studymyself{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0064743{col 38}{space 1}  -0.08{col 46}{space 3}0.922{col 54}{space 3}-.1483442{col 66}{space 3} .1796675
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}            parentsteach{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.1662913{col 38}{space 1}  -3.85{col 46}{space 3}0.002{col 54}{space 3}-.2557626{col 66}{space 3}-.0627718
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. /// Cognitive
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat followup_cog_std if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_cog = r(StatTotal)
{txt}  5{com}. 
. tabstat followup_cog_std if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_cog = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}{ralign 12:Variable} {...}
{c |}         N
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res}      145
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
   followup_c~d
N {res}          145
{reset}
{ralign 12:Variable} {...}
{c |}         N
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res}       98
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
   followup_c~d
N {res}           98
{reset}
{ralign 12:Variable} {...}
{c |}      Mean
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res}-.0920409
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
      followup_c~d
Mean {res}   -.09204085
{reset}
{ralign 12:Variable} {...}
{c |}      Mean
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res} .1361831
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
      followup_c~d
Mean {res}    .13618309
{reset}
{ralign 12:Variable} {...}
{c |}        SD
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res} 1.070796
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
    followup_c~d
SD {res}     1.070796
{reset}
{ralign 12:Variable} {...}
{c |}        SD
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res} .8725076
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
    followup_c~d
SD {res}    .87250763
{reset}
{com}. 
. matrix n_cog = J(1,1,.)
{txt}
{com}. forvalues i = 1/1 {c -(}
{txt}  2{com}.         matrix n_cog[1,`i'] = n_tr_cog[1,`i'] + n_ct_cog[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2282239{col 38}{space 1}  -1.36{col 46}{space 3}0.178{col 54}{space 3}-.5949467{col 66}{space 3} .1042338
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}.         
. matrix r2_followup_cog_std_temp = r(table)
{txt}
{com}. 
. 
. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix r2_followup_cog_std_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix r2_followup_cog_std_mean[1,`j'] = r2_followup_cog_std_temp[1,`j']
{txt}  3{com}. * standard error
. * matrix r2_followup_cog_std_se[1,`j'] = r2_followup_cog_std_temp[2,`j']
. * p value
. matrix r2_followup_cog_std_pv[1,`j'] = r2_followup_cog_std_temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}.     
. /// Non cognitive
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat followup_noncog_std RSES_std CPCS_std if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_noncog = r(StatTotal)
{txt}  5{com}. 
. tabstat followup_noncog_std RSES_std CPCS_std if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_noncog = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}      105       140       140
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   followup_n~d      RSES_std      CPCS_std
N {res}          105           140           140
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}       74        96        96
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   followup_n~d      RSES_std      CPCS_std
N {res}           74            96            96
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .1969319  .1591241  .1745941
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      followup_n~d      RSES_std      CPCS_std
Mean {res}    .19693189      .1591241     .17459415
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res}-.2794302 -.2320565  -.254617
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      followup_n~d      RSES_std      CPCS_std
Mean {res}   -.27943024    -.23205648    -.25461705
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} 1.006158  1.022691  1.008304
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    followup_n~d      RSES_std      CPCS_std
SD {res}    1.0061577     1.0226907     1.0083041
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} .9279901  .9228443  .9357831
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    followup_n~d      RSES_std      CPCS_std
SD {res}    .92799012     .92284427     .93578307
{reset}
{com}. 
. matrix n_noncog = J(1,3,.)
{txt}
{com}. forvalues i = 1/3 {c -(}
{txt}  2{com}.         matrix n_noncog[1,`i'] = n_tr_noncog[1,`i'] + n_ct_noncog[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in followup_noncog_std RSES_std CPCS_std{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}30{text}.{text} done{text} ({result:31})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}179
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 32
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}5.6
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}     followup_noncog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .4763621{col 38}{space 1}   2.08{col 46}{space 3}0.058{col 54}{space 3}-.0144745{col 66}{space 3} .9682468
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:29})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}236
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.2
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3911806{col 38}{space 1}   2.02{col 46}{space 3}0.068{col 54}{space 3}-.0320917{col 66}{space 3} .7926849
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}236
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.2
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .4292112{col 38}{space 1}   2.26{col 46}{space 3}0.040{col 54}{space 3} .0113741{col 66}{space 3} .8020083
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. 
. /// Behavioral
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat hyper if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_hyper = r(StatTotal)
{txt}  5{com}. 
. tabstat hyper if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_hyper = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}{ralign 12:Variable} {...}
{c |}         N
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res}      113
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
   hyper
N {res}   113
{reset}
{ralign 12:Variable} {...}
{c |}         N
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res}       71
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
   hyper
N {res}    71
{reset}
{ralign 12:Variable} {...}
{c |}      Mean
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res} .2654867
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
          hyper
Mean {res} .26548673
{reset}
{ralign 12:Variable} {...}
{c |}      Mean
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res} .0704225
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
          hyper
Mean {res} .07042254
{reset}
{ralign 12:Variable} {...}
{c |}        SD
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res}  .443559
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
        hyper
SD {res} .44355905
{reset}
{ralign 12:Variable} {...}
{c |}        SD
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res} .2576789
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
        hyper
SD {res} .25767885
{reset}
{com}. 
. matrix n_hyper = J(1,1,.)
{txt}
{com}. forvalues i = 1/1 {c -(}
{txt}  2{com}.         matrix n_hyper[1,`i'] = n_tr_hyper[1,`i'] + n_ct_hyper[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in hyper{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment if hypernoinfo == 0, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:29})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}184
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}5.6
{col 69}{txt}max{col 72} = {res} 12
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                   hyper{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1950642{col 38}{space 1}   3.37{col 46}{space 3}0.004{col 54}{space 3} .0791866{col 66}{space 3} .3123475
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. 
. // significant level
. 
. local outcome DT_score_pre_std rosen_pre_std cpcs_pre_std hhmember hhheadage hhheadeduyear q2a q2c q2h tutor study_other followup_cog_std RSES_std CPCS_std
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}.                 if r2_`dep'_pv[1,1]<=0.01 {c -(}
{txt}  3{com}.                         local star_`dep' %3s "***"
{txt}  4{com}.                 {c )-}
{txt}  5{com}.                 else if (r2_`dep'_pv[1,1]>0.01) & (r2_`dep'_pv[1,1]<=0.05) {c -(}
{txt}  6{com}.                         local star_`dep' %2s "**"
{txt}  7{com}.                 {c )-}
{txt}  8{com}.                 else if (r2_`dep'_pv[1,1]>0.05) & (r2_`dep'_pv[1,1]<=0.10) {c -(}
{txt}  9{com}.                         local star_`dep' %1s "*"
{txt} 10{com}.                 {c )-}
{txt} 11{com}.                 else {c -(}
{txt} 12{com}.                         local star_`dep'  ""
{txt} 13{com}.                 {c )-}
{txt} 14{com}. {c )-} 
{txt}
{com}. 
. set seed 1
{txt}
{com}. rwolf DT_score_pre_std rosen_pre_std cpcs_pre_std hhmember hhheadage hhheadeduyear, indepvar(treatment) reps(1000)
Bootstrap replications (1000). This may take some time.
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Romano-Wolf step-down adjusted p-values


Independent variable:  treatment
Outcome variables:   DT_score_pre_std rosen_pre_std cpcs_pre_std hhmember
{col 22}hhheadage hhheadeduyear
Number of resamples: 1000


{hline 78}
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
{hline 20}+{hline 57}
   {txt}DT_score_pre_std {c |}    {res}0.5518             0.5495              0.8312
      {txt}rosen_pre_std {c |}    {res}0.4689             0.4496              0.8312
       {txt}cpcs_pre_std {c |}    {res}0.0105             0.0160              0.0589
           {txt}hhmember {c |}    {res}0.1345             0.1469              0.4635
          {txt}hhheadage {c |}    {res}0.9229             0.9201              0.9201
      {txt}hhheadeduyear {c |}    {res}0.0494             0.0500              0.2478
{hline 78}
{txt}
{com}. set seed 1
{txt}
{com}. rwolf q2a q2c q2h tutor study_other followup_cog_std RSES_std CPCS_std, indepvar(treatment) reps(1000)
Bootstrap replications (1000). This may take some time.
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5
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Romano-Wolf step-down adjusted p-values


Independent variable:  treatment
Outcome variables:   q2a q2c q2h tutor study_other followup_cog_std RSES_std CPCS_std
Number of resamples: 1000


{hline 78}
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
{hline 20}+{hline 57}
                {txt}q2a {c |}    {res}0.7471             0.7493              0.7872
                {txt}q2c {c |}    {res}0.4722             0.4595              0.7872
                {txt}q2h {c |}    {res}0.2801             0.2717              0.5764
              {txt}tutor {c |}    {res}0.0573             0.0460              0.2298
        {txt}study_other {c |}    {res}0.6083             0.6234              0.7872
   {txt}followup_cog_std {c |}    {res}0.0809             0.0799              0.2697
           {txt}RSES_std {c |}    {res}0.0030             0.0030              0.0110
           {txt}CPCS_std {c |}    {res}0.0011             0.0020              0.0060
{hline 78}
{txt}
{com}. 
. 
. /// Table
> tempname hh2
{txt}
{com}. file open `hh2' using "$path_output/summary_stat.tex", write replace
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "% Author: Kazuma Takakura" _newline
{txt}
{com}. file write `hh2' "% Date: `c(current_date)'" _newline
{txt}
{com}. file write `hh2' "% Time: `c(current_time)'" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. 
. file write `hh2' "\begin{c -(}table{c )-}[h!]\footnotesize" _newline
{txt}
{com}. file write `hh2' "  \centering" _newline
{txt}
{com}. file write `hh2' "  \caption{c -(}Summary Statistics{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:sumstat{c )-}" _newline
{txt}
{com}. file write `hh2' "\scalebox{c -(}1{c )-}{c -(}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}threeparttable{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "\begin{c -(}tabular{c )-}{c -(}lccccc{c )-}\toprule" _newline
{txt}
{com}. 
.   
. file write `hh2' " Dependent Variable & Treatment &  Control  & Difference & N   \\\midrule\midrule" _newline
{txt}
{com}. file write `hh2' " Panel A: Baseline & & & &   \\ " _newline
{txt}
{com}. file write `hh2' " DT score^{c -(}a{c )-} & " %04.3f (mean_tr_bl[1,1]) " & " %04.3f (mean_ct_bl[1,1]) " & " %04.3f (r2_DT_score_pre_std_mean[1,1]) `star_DT_score_pre_std' " & " (n_bl[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_bl[1,1]) " ] & [ " %04.3f (sd_ct_bl[1,1]) " ] & ( " %04.3f (r2_DT_score_pre_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.831) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. 
. file write `hh2' " RSES score^{c -(}a{c )-} & " %04.3f (mean_tr_bl[1,2]) " & " %04.3f (mean_ct_bl[1,2]) " & " %04.3f (r2_rosen_pre_std_mean[1,1]) `star_rosen_pre_std' " & "  (n_bl[1,2]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_bl[1,2]) " ] & [ " %04.3f (sd_ct_bl[1,2]) " ] & ( " %04.3f (r2_rosen_pre_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.831) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " CPCS score^{c -(}a{c )-} & " %04.3f (mean_tr_bl[1,3]) " & " %04.3f (mean_ct_bl[1,3]) " & " %04.3f (r2_cpcs_pre_std_mean[1,1]) `star_cpcs_pre_std' " & "  (n_bl[1,3]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_bl[1,3]) " ] & [ " %04.3f (sd_ct_bl[1,3]) " ] & ( " %04.3f (r2_cpcs_pre_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.059) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Household size & " %04.3f (mean_tr_parent[1,1]) " & " %04.3f (mean_ct_parent[1,1]) " & " %04.3f (r2_hhmember_mean[1,1]) `star_hhmember'  " & " (n_parent[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_parent[1,1]) " ] & [ " %04.3f (sd_ct_parent[1,1]) " ] & ( " %04.3f (r2_hhmember_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.464) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Household head age & " %04.3f (mean_tr_parent[1,2]) " & " %04.3f (mean_ct_parent[1,2]) " & " %04.3f (r2_hhheadage_mean[1,1]) `star_hhheadage' " & "  (n_parent[1,2]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_parent[1,2]) " ] & [ " %04.3f (sd_ct_parent[1,2]) " ] & ( " %04.3f (r2_hhheadage_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.920) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Household head education & " %04.3f (mean_tr_parent[1,3]) " & " %04.3f (mean_ct_parent[1,3]) " & " %04.3f (r2_hhheadeduyear_mean[1,1]) `star_hhheadeduyear' " & "  (n_parent[1,3]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_parent[1,3]) " ] & [ " %04.3f (sd_ct_parent[1,3]) " ] & ( " %04.3f (r2_hhheadeduyear_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.248) " \{c )-} &   \\ " _newline
{txt}
{com}. file write `hh2' " \\ "_newline
{txt}
{com}. 
. file write `hh2' " Panel B: Follow-up & & & &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " School attendance & " %04.3f (mean_tr_school[1,1]) " & " %04.3f (mean_ct_school[1,1]) " & " %04.3f (r2_q2a_mean[1,1]) `star_q2a' " & " (n_school[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_school[1,1]) " ] & [ " %04.3f (sd_ct_school[1,1]) " ] & ( " %04.3f (r2_q2a_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.787) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Grade repeat & " %04.3f (mean_tr_school[1,3]) " & " %04.3f (mean_ct_school[1,3]) " & " %04.3f (r2_q2c_mean[1,1]) `star_q2c' " & "  (n_school[1,3]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_school[1,3]) " ] & [ " %04.3f (sd_ct_school[1,3]) " ] & ( " %04.3f (r2_q2c_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.787) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Drop out & " %04.3f (mean_tr_school[1,4]) " & " %04.3f (mean_ct_school[1,4]) " & " %04.3f (r2_q2h_mean[1,1]) `star_q2h'  " & "  (n_school[1,4]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_school[1,4]) " ] & [ " %04.3f (sd_ct_school[1,4]) " ] & ( " %04.3f (r2_q2h_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.576) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Tutoring & " %04.3f (mean_tr_study[1,1]) " & " %04.3f (mean_ct_study[1,1]) " & " %04.3f (r2_tutor_mean[1,1]) `star_tutor'  " & " (n_study[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_study[1,1]) " ] & [ " %04.3f (sd_ct_study[1,1]) " ] & ( " %04.3f (r2_tutor_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.230) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Self-study & " %04.3f (mean_tr_study[1,2]) " & " %04.3f (mean_ct_study[1,2]) " & " %04.3f (r2_study_other_mean[1,1]) `star_study_other' " & "  (n_study[1,2]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_study[1,2]) " ] & [ " %04.3f (sd_ct_study[1,2]) " ] & ( " %04.3f (r2_study_other_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.787) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Rapid math test score^{c -(}a{c )-} & " %04.3f (mean_tr_cog[1,1]) " & " %04.3f (mean_ct_cog[1,1]) " & " %04.3f (r2_followup_cog_std_mean[1,1]) `star_followup_cog_std'  "  & "  (n_cog[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_cog[1,1]) " ] & [ " %04.3f (sd_ct_cog[1,1]) " ] & ( " %04.3f (r2_followup_cog_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.270) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " RSES score^{c -(}a{c )-} & " %04.3f (mean_tr_noncog[1,2]) " & " %04.3f (mean_ct_noncog[1,2]) " & " %04.3f (r2_RSES_std_mean[1,1])   `star_RSES_std' " & " (n_noncog[1,2]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_noncog[1,2]) " ] & [ " %04.3f (sd_ct_noncog[1,2]) " ] & ( " %04.3f (r2_RSES_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.011) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " CPCS score^{c -(}a{c )-} & " %04.3f (mean_tr_noncog[1,3]) " & " %04.3f (mean_ct_noncog[1,3]) " & " %04.3f (r2_CPCS_std_mean[1,1])   `star_CPCS_std' "&  " (n_noncog[1,3]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_noncog[1,3]) " ] & [ " %04.3f (sd_ct_noncog[1,3]) " ] & ( " %04.3f (r2_CPCS_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.006) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. 
. file write `hh2' "\midrule" _newline
{txt}
{com}. file write `hh2' "\end{c -(}tabular{c )-}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\item (a) Variables are standardized using the average and variance of the whole follow-up sample in the February 2022 survey. " _newline
{txt}
{com}. file write `hh2' "\item (b) Standard deviations are reported in square brackets.  " _newline
{txt}
{com}. file write `hh2' "\item (c) Wild clustered bootstrap p-values are reported within parentheses. Clusters are schools at the baseline. There are 34 clusters. " _newline
{txt}
{com}. file write `hh2' "\item (d) Romano-Wolf multiple hypothesis testing p-values are reported in curly brackets. This test is conducted separately for the baseline variables and the follow-up variables." _newline
{txt}
{com}. file write `hh2' "\item (e) Statistical significance is indicated by stars based on the wild clustered bootstrap p-values reported in parentheses: $*$ denotes significance at the 10\% level, $∗∗$ at the 5\% level, and $∗∗∗$ at the 1\% level.  " _newline
{txt}
{com}. file write `hh2' "\end{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}threeparttable{c )-}" _newline
{txt}
{com}. file write `hh2' "{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}table{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. file close `hh2'
{txt}
{com}. 
{txt}end of do-file

{com}. 
{txt}end of do-file

{com}. do "/var/folders/mb/s9qljd594gbfyhl2dqnnwlcw0000gn/T//SD56186.000000"
{txt}
{com}. version 18.5
{txt}
{com}. clear all
{res}{txt}
{com}. set more off
{txt}
{com}. 
. 
. * set the path to global
. 
. global path_replication "/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package"
{txt}
{com}. 
. global path_output "$path_replication/outputs"
{txt}
{com}. 
. global path_data "$path_replication/data"
{txt}
{com}. 
. global path_do "$path_replication/do_files"
{txt}
{com}. 
. adopath + "$path_replication/ado"
{txt}  [1]  (BASE)      "{res}/Applications/Stata/ado/base/{txt}"
  [2]  (SITE)      "{res}/Applications/Stata/ado/site/{txt}"
  [3]              "{res}.{txt}"
  [4]  (PERSONAL)  "{res}/Users/takakurakazuma/Documents/Stata/ado/personal/{txt}"
  [5]  (PLUS)      "{res}/Users/takakurakazuma/Library/Application Support/Stata/ado/plus/{txt}"
  [6]  (OLDPLACE)  "{res}~/ado/{txt}"
  [7]              "{res}CHANGE TO YOUR PATH/ado{txt}"
  [8]              "{res}/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/ado{txt}"

{com}. 
. 
. 
. * log using "$path_output/log_all", replace
. 
. set seed 123
{txt}
{com}. 
. **************************************************
. 
. 
. *** run the code for cleaning.
. 
. do "$path_do/1_data_cleaning_students.do"
{txt}
{com}. clear all
{res}{txt}
{com}. set more off
{txt}
{com}. 
. 
. /// prepare baseline teacher information
> use "$path_data/original_teacher.dta", clear
{txt}
{com}. drop if endline == 1
{txt}(1,004 observations deleted)

{com}. keep student_no age_tchr gender_tchr edu_tchr
{txt}
{com}. sort student_no
{txt}
{com}. save "$path_data/temp/teacher", replace
{txt}{p 0 4 2}
file {bf}
/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/data/temp/teacher.dta{rm}
saved
{p_end}

{com}. 
. import excel "$path_data/followup_students_master.xlsx", clear first
{res}{text}(255 vars, 287 obs)

{com}. gen student_no = q1b
{txt}
{com}. 
. /// remove imcomplete interview
> drop if q0a == "319003"
{txt}(1 observation deleted)

{com}. 
. /// Yes==1, No==0, Dont know ==.
> recode q2a q2c q2h q3a q3e q4a q7a q7b q9a1 q9a3 q9b1 (2=0)
{txt}(127 changes made to {bf:q2a})
(268 changes made to {bf:q2c})
(173 changes made to {bf:q2h})
(170 changes made to {bf:q3a})
(41 changes made to {bf:q3e})
(24 changes made to {bf:q4a})
(56 changes made to {bf:q7a})
(11 changes made to {bf:q7b})
(24 changes made to {bf:q9a1})
(267 changes made to {bf:q9a3})
(0 changes made to {bf:q9b1})

{com}. recode q9b1 (3=.)
{txt}(2 changes made to {bf:q9b1})

{com}. 
. 
. 
. 
. // other changes
. 
. gen PSC_grade = q2k2
{txt}(62 missing values generated)

{com}. replace PSC_grade ="0" if q2k2== "Auto"
{txt}(5 real changes made)

{com}. replace PSC_grade ="0" if q2k2== "mone nai"
{txt}(1 real change made)

{com}. replace PSC_grade ="0" if q2k2== ""
{txt}(62 real changes made)

{com}. replace PSC_grade ="3.08" if q2k2=="3.o8"
{txt}(1 real change made)

{com}. destring PSC_grade, replace
{txt}PSC_grade: all characters numeric; {res}replaced {txt}as {res}double
{txt}
{com}. recode PSC_grade(0=.)
{txt}(68 changes made to {bf:PSC_grade})

{com}. 
. gen JSC_grade = q2l2
{txt}(146 missing values generated)

{com}. gen JSC_auto = 0
{txt}
{com}. replace JSC_auto = 1 if q2l2 == "Ato pas"
{txt}(3 real changes made)

{com}. replace JSC_auto = 1 if q2l2 == "Atou pass"
{txt}(1 real change made)

{com}. replace JSC_auto = 1 if q2l2 == "Auto"
{txt}(32 real changes made)

{com}. replace JSC_auto = 1 if q2l2 == "Auto  pass"
{txt}(4 real changes made)

{com}. replace JSC_auto = 1 if q2l2 == "Auto Pass"
{txt}(8 real changes made)

{com}. replace JSC_auto = 1 if q2l2 == "Auto pass"
{txt}(22 real changes made)

{com}. replace JSC_auto = 1 if q2l2 == "Autopash."
{txt}(1 real change made)

{com}. replace JSC_auto = 1 if q2l2 == "Autopass"
{txt}(1 real change made)

{com}. replace JSC_auto = 1 if q2l2 == "auto pass"
{txt}(11 real changes made)

{com}. replace JSC_auto = 1 if q2l2 == "result school thake deyni"
{txt}(1 real change made)

{com}. replace JSC_grade = "0" if JSC_auto == 1
{txt}(84 real changes made)

{com}. replace JSC_grade = "0" if q2l2 == ""
{txt}(146 real changes made)

{com}. destring JSC_grade, replace
{txt}JSC_grade: all characters numeric; {res}replaced {txt}as {res}double
{txt}
{com}. recode JSC_grade(0=.)
{txt}(230 changes made to {bf:JSC_grade})

{com}. 
. 
. local q5an 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
{txt}
{com}. foreach i in `q5an'{c -(}
{txt}  2{com}. gen q5a_`i' = 1 if q5a==`i'
{txt}  3{com}. recode q5a_`i'(.=0)
{txt}  4{com}. {c )-}
{txt}(285 missing values generated)
(285 changes made to {bf:q5a_1})
(270 missing values generated)
(270 changes made to {bf:q5a_2})
(265 missing values generated)
(265 changes made to {bf:q5a_3})
(188 missing values generated)
(188 changes made to {bf:q5a_4})
(264 missing values generated)
(264 changes made to {bf:q5a_5})
(284 missing values generated)
(284 changes made to {bf:q5a_6})
(282 missing values generated)
(282 changes made to {bf:q5a_7})
(263 missing values generated)
(263 changes made to {bf:q5a_8})
(285 missing values generated)
(285 changes made to {bf:q5a_9})
(286 missing values generated)
(286 changes made to {bf:q5a_10})
(286 missing values generated)
(286 changes made to {bf:q5a_11})
(217 missing values generated)
(217 changes made to {bf:q5a_12})
(279 missing values generated)
(279 changes made to {bf:q5a_13})
(286 missing values generated)
(286 changes made to {bf:q5a_14})
(284 missing values generated)
(284 changes made to {bf:q5a_15})
(286 missing values generated)
(286 changes made to {bf:q5a_16})

{com}. 
. local q5bn 1 2 3 4 5 6 7 8 9
{txt}
{com}. foreach i in `q5bn'{c -(}
{txt}  2{com}. gen q5b_`i' = 1 if q5b==`i'
{txt}  3{com}. recode q5b_`i'(.=0)
{txt}  4{com}. {c )-}
{txt}(243 missing values generated)
(243 changes made to {bf:q5b_1})
(189 missing values generated)
(189 changes made to {bf:q5b_2})
(260 missing values generated)
(260 changes made to {bf:q5b_3})
(281 missing values generated)
(281 changes made to {bf:q5b_4})
(231 missing values generated)
(231 changes made to {bf:q5b_5})
(285 missing values generated)
(285 changes made to {bf:q5b_6})
(284 missing values generated)
(284 changes made to {bf:q5b_7})
(277 missing values generated)
(277 changes made to {bf:q5b_8})
(283 missing values generated)
(283 changes made to {bf:q5b_9})

{com}. 
. 
. 
. gen q6a1_correct = 1 if q6a1==10800
{txt}(88 missing values generated)

{com}. gen q6a2_correct = 1 if q6a2==9
{txt}(43 missing values generated)

{com}. gen q6a3a_correct = 1 if q6a3a==70
{txt}(70 missing values generated)

{com}. gen q6a3b_correct = 1 if q6a3b==50
{txt}(46 missing values generated)

{com}. gen q6a4_correct = 1 if q6a4==20
{txt}(211 missing values generated)

{com}. gen q6a5_correct = 1 if q6a5==5
{txt}(224 missing values generated)

{com}. 
. recode q6a1_correct q6a2_correct q6a3a_correct q6a3b_correct q6a4_correct q6a5_correct (.=0)
{txt}(88 changes made to {bf:q6a1_correct})
(43 changes made to {bf:q6a2_correct})
(70 changes made to {bf:q6a3a_correct})
(46 changes made to {bf:q6a3b_correct})
(211 changes made to {bf:q6a4_correct})
(224 changes made to {bf:q6a5_correct})

{com}. 
. save "$path_data/temp/followup_student_data", replace
{txt}{p 0 4 2}
file {bf}
/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/data/temp/followup_student_data.dta{rm}
saved
{p_end}

{com}.  
. 
. import excel "$path_data/followup_students_extra.xlsx",  clear first
{res}{text}(50 vars, 222 obs)

{com}. drop if q1b==1223 & _index==64
{txt}(1 observation deleted)

{com}. drop if q1b==2804 & _index==116
{txt}(1 observation deleted)

{com}. keep q1b q3c1new q3c2new q3e _index
{txt}
{com}. rename q3e q3enew
{res}{txt}
{com}. recode q3enew(2=0)
{txt}(155 changes made to {bf:q3enew})

{com}. destring q1b, replace
{txt}q1b already numeric; no {res}replace
{txt}
{com}. merge 1:1 q1b using "$path_data/temp/followup_student_data"
{res}
{txt}{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}              68
{txt}{col 9}from master{col 30}{res}               1{txt}  (_merge==1)
{col 9}from using{col 30}{res}              67{txt}  (_merge==2)

{col 5}Matched{col 30}{res}             219{txt}  (_merge==3)
{col 5}{hline 41}

{com}. save "$path_data/temp/followup_student_data", replace
{txt}{p 0 4 2}
file {bf}
/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/data/temp/followup_student_data.dta{rm}
saved
{p_end}

{com}. 
. 
. // check the accuracy of q3c
. drop if _merge==1
{txt}(1 observation deleted)

{com}. drop _merge
{txt}
{com}. sum q3c1new

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}q3c1new {c |}{res}         94    5.851064    .6038843          4          7
{txt}
{com}. sum q3c2new

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}q3c2new {c |}{res}         94    9.829787    3.999028          4         21
{txt}
{com}. * tab treatment q3enew
. tab q3e q3enew

           {txt}{c |}          q3e
       q3e {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}        14         11 {txt}{c |}{res}        25 
{txt}         1 {c |}{res}       141         53 {txt}{c |}{res}       194 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       155         64 {txt}{c |}{res}       219 
{txt}
{com}. 
. // merge with baseline & endline
. use "$path_data/original_raw_score", clear
{txt}
{com}. keep student_no DT_score_pre cpcs_pre rosen_pre
{txt}
{com}. save "$path_data/temp/rawscore", replace
{txt}{p 0 4 2}
file {bf}
/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/data/temp/rawscore.dta{rm}
saved
{p_end}

{com}. 
. use "$path_data/temp/followup_student_data", clear
{txt}
{com}. drop _merge 
{txt}
{com}. merge 1:1 student_no using "$path_data/original_main"
{res}{txt}(label {bf:{txt}_merge} already defined)

{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}             812
{txt}{col 9}from master{col 30}{res}              44{txt}  (_merge==1)
{col 9}from using{col 30}{res}             768{txt}  (_merge==2)

{col 5}Matched{col 30}{res}             243{txt}  (_merge==3)
{col 5}{hline 41}

{com}. rename _merge _merge_base_character
{res}{txt}
{com}. merge 1:1 student_no using "$path_data/temp/rawscore"
{res}
{txt}{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}              44
{txt}{col 9}from master{col 30}{res}              44{txt}  (_merge==1)
{col 9}from using{col 30}{res}               0{txt}  (_merge==2)

{col 5}Matched{col 30}{res}           1,011{txt}  (_merge==3)
{col 5}{hline 41}

{com}. rename _merge _merge_base_score
{res}{txt}
{com}. tab treatment _merge_base_score

           {txt}{c |}  Matching
           {c |}   result
           {c |} from merge
 treatment {c |} Matched ( {c |}     Total
{hline 11}{c +}{hline 11}{c +}{hline 10}
         0 {c |}{res}       478 {txt}{c |}{res}       478 
{txt}         1 {c |}{res}       526 {txt}{c |}{res}       526 
{txt}{hline 11}{c +}{hline 11}{c +}{hline 10}
     Total {c |}{res}     1,004 {txt}{c |}{res}     1,004 
{txt}
{com}. 
. gen attrition = 0 if _merge_base_character == 3
{txt}(812 missing values generated)

{com}. recode attrition (.=1)
{txt}(812 changes made to {bf:attrition})

{com}. tab attrition

  {txt}attrition {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        243       23.03       23.03
{txt}          1 {c |}{res}        812       76.97      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,055      100.00
{txt}
{com}. 
. // fix missing values using baseline information
. replace school_no = 18 if school_no == . & student_no == 1817
{txt}(1 real change made)

{com}. replace treatment = 1 if student_no == 1817
{txt}(1 real change made)

{com}. replace grade = 2 if student_no == 1817
{txt}(1 real change made)

{com}. replace branch1 = 0 if student_no == 1817
{txt}(1 real change made)

{com}. replace branch2 = 0 if student_no == 1817
{txt}(1 real change made)

{com}. replace branch3 = 1 if student_no == 1817
{txt}(1 real change made)

{com}. replace branch4 = 0 if student_no == 1817
{txt}(1 real change made)

{com}. 
. save "$path_data/temp/student_unbalance", replace
{txt}{p 0 4 2}
file {bf}
/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/data/temp/student_unbalance.dta{rm}
saved
{p_end}

{com}. 
. 
. // keep balanced panel
. keep if attrition == 0
{txt}(812 observations deleted)

{com}. 
. save "$path_data/temp/endline_followup_student_data", replace
{txt}{p 0 4 2}
file {bf}
/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/data/temp/endline_followup_student_data.dta{rm}
saved
{p_end}

{com}. 
. 
. gen followup_cog = q6a1_correct + q6a2_correct + q6a3a_correct + q6a3b_correct + q6a4_correct + q6a5_correct
{txt}
{com}. 
. /// non-cog
> // positive: 2,3,5,7,10,11,12,17,18,20,21,22,23,25,26,27,28,29,32,33,34,36,37,39
. // positive-cog:1,13,14,19,24,
. // negative: 4,6,8,9,30,31,35,38,40
. 
. local q99 q6c1 q6c2 q6c3 q6c4 q6c5 q6c6 q6c7 q6c8 q6c9 q6c10 q6c11 q6c12 q6c13 q6c14 q6c15 q6c16 q6c17 q6c18 q6c19 q6c20 ///
> q6c21 q6c22 q6c23 q6c24 q6c25 q6c26 q6c27 q6c28 q6c29 q6c30 q6c31 q6c32 q6c33 q6c34 q6c35 q6c36 q6c37 q6c38 q6c39 q6c40 ///
> q8a1a q8a2a q8a3a q8a4a q8a5a
{txt}
{com}. 
. foreach y in `q99'{c -(}
{txt}  2{com}. replace `y'=.  if `y'==99
{txt}  3{com}. {c )-}
{txt}(1 real change made, 1 to missing)
(1 real change made, 1 to missing)
(1 real change made, 1 to missing)
(0 real changes made)
(3 real changes made, 3 to missing)
(2 real changes made, 2 to missing)
(0 real changes made)
(2 real changes made, 2 to missing)
(0 real changes made)
(0 real changes made)
(1 real change made, 1 to missing)
(1 real change made, 1 to missing)
(0 real changes made)
(0 real changes made)
(6 real changes made, 6 to missing)
(8 real changes made, 8 to missing)
(1 real change made, 1 to missing)
(12 real changes made, 12 to missing)
(12 real changes made, 12 to missing)
(5 real changes made, 5 to missing)
(1 real change made, 1 to missing)
(2 real changes made, 2 to missing)
(2 real changes made, 2 to missing)
(7 real changes made, 7 to missing)
(13 real changes made, 13 to missing)
(1 real change made, 1 to missing)
(2 real changes made, 2 to missing)
(1 real change made, 1 to missing)
(8 real changes made, 8 to missing)
(1 real change made, 1 to missing)
(0 real changes made)
(1 real change made, 1 to missing)
(0 real changes made)
(0 real changes made)
(26 real changes made, 26 to missing)
(16 real changes made, 16 to missing)
(0 real changes made)
(0 real changes made)
(1 real change made, 1 to missing)
(20 real changes made, 20 to missing)
(3 real changes made, 3 to missing)
(2 real changes made, 2 to missing)
(4 real changes made, 4 to missing)
(0 real changes made)
(1 real change made, 1 to missing)

{com}. 
. gen noncog4 = 5 - q6c4
{txt}
{com}. gen noncog6 = 5 - q6c6
{txt}(2 missing values generated)

{com}. gen noncog8 = 5 - q6c8
{txt}(2 missing values generated)

{com}. gen noncog9 = 5 - q6c9
{txt}
{com}. gen noncog30 = 5 - q6c30
{txt}(1 missing value generated)

{com}. gen noncog31 = 5 - q6c31
{txt}
{com}. gen noncog35 = 5 - q6c35
{txt}(26 missing values generated)

{com}. gen noncog38 = 5 - q6c38
{txt}
{com}. gen noncog40 = 5 - q6c40
{txt}(20 missing values generated)

{com}. 
. gen followup_noncog = q6c1+q6c2+q6c3+noncog4+q6c5+noncog6+q6c7+noncog8+noncog9+q6c10+q6c11+q6c12+q6c13+q6c14+q6c17+q6c18+q6c19+q6c20+q6c21+q6c22+q6c23+q6c24+q6c25+q6c26+q6c27+q6c28+q6c29+noncog30+noncog31+q6c32+q6c33+q6c34+noncog35+q6c36+q6c37+noncog38+noncog40+q6c39
{txt}(64 missing values generated)

{com}. gen followup_noncog2 = q6c2+q6c3+noncog4+q6c5+noncog6+q6c7+noncog8+noncog9+q6c10+q6c11+q6c12+q6c17+q6c18+q6c20+q6c21+q6c22+q6c23+q6c25+q6c26+q6c27+q6c28+q6c29+noncog30+noncog31+q6c32+q6c33+q6c34+noncog35+q6c36+q6c37+noncog38+noncog40+q6c39
{txt}(63 missing values generated)

{com}. 
. sum followup_noncog followup_noncog2

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
followup_n~g {c |}{res}        179    64.49721    16.11419         31        111
{txt}followup_n~2 {c |}{res}        180    55.78889    14.00558         25         94
{txt}
{com}. 
. replace followup_noncog = 190 - followup_noncog
{txt}(179 real changes made)

{com}. replace followup_noncog2 = 180 - followup_noncog2
{txt}(179 real changes made)

{com}. 
. gen RSES = 40 - q6c2 - q6c3 - noncog4 - noncog6 - noncog8 - noncog9 - q6c10 - q6c11
{txt}(7 missing values generated)

{com}. gen CPCS = 50 - q6c2 - q6c3 - noncog4 - q6c5 - noncog6 -q6c7 - noncog8 - noncog9 - q6c10 - q6c11
{txt}(7 missing values generated)

{com}. 
. /// variables for study situation
> gen tutor = 1 if q3a == 1
{txt}(149 missing values generated)

{com}. gen study_other = 1 if q4a == 1
{txt}(134 missing values generated)

{com}. gen study_affect_covid = 1 if q9a21 == 1
{txt}(90 missing values generated)

{com}. gen hometutoring = 1 if q9a2a1 == 1
{txt}(215 missing values generated)

{com}. gen onlineclass = 1 if q9a2a2 == 1
{txt}(223 missing values generated)

{com}. gen studymyself = 1 if q9a2a3 == 1
{txt}(115 missing values generated)

{com}. gen parentsteach = 1 if q9a2a4 == 1
{txt}(220 missing values generated)

{com}. recode tutor study_other study_affect_covid hometutoring onlineclass studymyself parentsteach (.=0)
{txt}(149 changes made to {bf:tutor})
(134 changes made to {bf:study_other})
(90 changes made to {bf:study_affect_covid})
(215 changes made to {bf:hometutoring})
(223 changes made to {bf:onlineclass})
(115 changes made to {bf:studymyself})
(220 changes made to {bf:parentsteach})

{com}. 
. /// other variable
> gen phone_survey = 1 if q1a0 == 2
{txt}(184 missing values generated)

{com}. recode phone_survey (.=0)
{txt}(184 changes made to {bf:phone_survey})

{com}. 
. /// Standardization
> egen DT_score_pre_mean = mean(DT_score_pre)
{txt}
{com}. egen DT_score_pre_sd = sd(DT_score_pre)
{txt}
{com}. gen DT_score_pre_std = (DT_score_pre-DT_score_pre_mean)/DT_score_pre_sd
{txt}(4 missing values generated)

{com}. drop DT_score_pre_mean DT_score_pre_sd 
{txt}
{com}. 
. egen cpcs_pre_mean = mean(cpcs_pre)
{txt}
{com}. egen cpcs_pre_sd = sd(cpcs_pre)
{txt}
{com}. gen cpcs_pre_std = (cpcs_pre-cpcs_pre_mean)/cpcs_pre_sd
{txt}
{com}. drop cpcs_pre_mean cpcs_pre_sd 
{txt}
{com}. 
. egen rosen_pre_mean = mean(rosen_pre)
{txt}
{com}. egen rosen_pre_sd = sd(rosen_pre)
{txt}
{com}. gen rosen_pre_std = (rosen_pre-rosen_pre_mean)/rosen_pre_sd
{txt}
{com}. drop rosen_pre_mean rosen_pre_sd 
{txt}
{com}. 
. egen followup_cog_mean = mean(followup_cog)
{txt}
{com}. egen followup_cog_sd = sd(followup_cog)
{txt}
{com}. gen followup_cog_std = (followup_cog-followup_cog_mean)/followup_cog_sd
{txt}
{com}. drop followup_cog_mean followup_cog_sd 
{txt}
{com}. 
. egen followup_noncog_mean = mean(followup_noncog)
{txt}
{com}. egen followup_noncog_sd = sd(followup_noncog)
{txt}
{com}. gen followup_noncog_std = (followup_noncog - followup_noncog_mean)/followup_noncog_sd
{txt}(64 missing values generated)

{com}. drop followup_noncog_mean followup_noncog_sd 
{txt}
{com}. 
. egen CPCS_mean = mean(CPCS)
{txt}
{com}. egen CPCS_sd = sd(CPCS)
{txt}
{com}. gen CPCS_std = (CPCS - CPCS_mean)/CPCS_sd
{txt}(7 missing values generated)

{com}. drop CPCS_mean CPCS_sd 
{txt}
{com}. 
. egen RSES_mean = mean(RSES)
{txt}
{com}. egen RSES_sd = sd(RSES)
{txt}
{com}. gen RSES_std = (RSES-RSES_mean)/RSES_sd
{txt}(7 missing values generated)

{com}. drop RSES_mean RSES_sd 
{txt}
{com}. 
. /// missing
> gen DT_score_pre_std_missing_dummy = 1 if DT_score_pre_std == .
{txt}(239 missing values generated)

{com}. gen cpcs_pre_std_missing_dummy = 1 if cpcs_pre_std == .
{txt}(243 missing values generated)

{com}. gen rosen_pre_std_missing_dummy = 1 if rosen_pre_std == .
{txt}(243 missing values generated)

{com}. recode DT_score_pre_std_missing_dummy cpcs_pre_std_missing_dummy rosen_pre_std_missing_dummy (.=0)
{txt}(239 changes made to {bf:DT_score_pre_std_missing_dummy})
(243 changes made to {bf:cpcs_pre_std_missing_dummy})
(243 changes made to {bf:rosen_pre_std_missing_dummy})

{com}. 
. gen DT_score_pre_std_missing_0 = DT_score_pre_std if DT_score_pre_std_missing == 0
{txt}(4 missing values generated)

{com}. gen cpcs_pre_std_missing_0 = cpcs_pre_std if cpcs_pre_std != .
{txt}
{com}. gen rosen_pre_std_missing_0 = rosen_pre_std if rosen_pre_std != .
{txt}
{com}. recode DT_score_pre_std_missing_0 cpcs_pre_std_missing_0 rosen_pre_std_missing_0 (.=0)
{txt}(4 changes made to {bf:DT_score_pre_std_missing_0})
(0 changes made to {bf:cpcs_pre_std_missing_0})
(0 changes made to {bf:rosen_pre_std_missing_0})

{com}. 
. gen hyper = 1 if q7d2a == 1 & q7d2b == 2
{txt}(231 missing values generated)

{com}. replace hyper = 1 if q7d2a == 1 & q7d2b == 3
{txt}(10 real changes made)

{com}. replace hyper = 1 if q7d2a == 2 & q7d2b == 3
{txt}(13 real changes made)

{com}. gen hypernoinfo = 1 if q7d2a == .
{txt}(184 missing values generated)

{com}. recode hyper hypernoinfo (.=0)
{txt}(208 changes made to {bf:hyper})
(184 changes made to {bf:hypernoinfo})

{com}. replace hyper = . if hypernoinfo == 1
{txt}(59 real changes made, 59 to missing)

{com}. 
. 
. 
. /// merge teacher information
> merge 1:1 student_no using "$path_data/temp/teacher"
{res}
{txt}{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}             763
{txt}{col 9}from master{col 30}{res}               1{txt}  (_merge==1)
{col 9}from using{col 30}{res}             762{txt}  (_merge==2)

{col 5}Matched{col 30}{res}             242{txt}  (_merge==3)
{col 5}{hline 41}

{com}. rename _merge _merge_teacher
{res}{txt}
{com}. recode age_tchr(.=0)
{txt}(33 changes made to {bf:age_tchr})

{com}. gen age_tchr_missing_dummy = 1 if age_tchr == 0
{txt}(972 missing values generated)

{com}. recode age_tchr_missing_dummy(.=0)
{txt}(972 changes made to {bf:age_tchr_missing_dummy})

{com}. 
. 
. save "$path_data/temp/followup_student_baseline_score_missing_dummy", replace
{txt}{p 0 4 2}
file {bf}
/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/data/temp/followup_student_baseline_score_missing_dummy.dta{rm}
saved
{p_end}

{com}. 
. 
. 
. 
{txt}end of do-file

{com}. 
. do "$path_do/1_data_cleaning_parents.do"
{txt}
{com}. clear all
{res}{txt}
{com}. set more off
{txt}
{com}. 
. import excel "$path_data/followup_parents_master.xlsx", clear first
{res}{text}(593 vars, 230 obs)

{com}. 
. local q3888999 b3 b7 ///
> cm1_2 cm1_3 cm1_4 cm1_5 cm1_6 cm1_7 cm1_8 cm1_9 cm1_10 cm1_11 ///
> cm2_2 cm2_3 cm2_4 cm2_5 cm2_6 cm2_7 cm2_8 cm2_9 cm2_10 cm2_11 ///
> cm3_2 cm3_3 cm3_4 cm3_5 cm3_6 cm3_7 cm3_8 cm3_9 cm3_10 cm3_11 ///
> cm4_2 cm4_3 cm4_4 cm4_5 cm4_6 cm4_7 cm4_8 cm4_9 cm4_10 cm4_11 ///
> e2 f2_1
{txt}
{com}. 
. local yesno f1_1 f1_3 f2_1
{txt}
{com}. 
. local missingzero e9_1 e9_2 e9_3 e9_4 e9_5 e9_6 e9_7 e9_8 e9_9
{txt}
{com}. 
. foreach y in `q3888999'{c -(}
{txt}  2{com}. replace `y'=.  if `y'==3
{txt}  3{com}. replace `y'=.  if `y'==888
{txt}  4{com}. replace `y'=.  if `y'==999
{txt}  5{com}. {c )-}
{txt}(21 real changes made, 21 to missing)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(1 real change made, 1 to missing)
(3 real changes made, 3 to missing)
(0 real changes made)
(0 real changes made)
(5 real changes made, 5 to missing)
(0 real changes made)
(0 real changes made)
(5 real changes made, 5 to missing)
(0 real changes made)
(3 real changes made, 3 to missing)
(2 real changes made, 2 to missing)
(0 real changes made)
(1 real change made, 1 to missing)
(4 real changes made, 4 to missing)
(0 real changes made)
(4 real changes made, 4 to missing)
(1 real change made, 1 to missing)
(0 real changes made)
(2 real changes made, 2 to missing)
(2 real changes made, 2 to missing)
(0 real changes made)
(2 real changes made, 2 to missing)
(4 real changes made, 4 to missing)
(0 real changes made)
(8 real changes made, 8 to missing)
(5 real changes made, 5 to missing)
(0 real changes made)
(3 real changes made, 3 to missing)
(2 real changes made, 2 to missing)
(0 real changes made)
(0 real changes made)
(1 real change made, 1 to missing)
(0 real changes made)
(1 real change made, 1 to missing)
(3 real changes made, 3 to missing)
(0 real changes made)
(1 real change made, 1 to missing)
(2 real changes made, 2 to missing)
(0 real changes made)
(1 real change made, 1 to missing)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(3 real changes made, 3 to missing)
(0 real changes made)
(2 real changes made, 2 to missing)
(3 real changes made, 3 to missing)
(0 real changes made)
(1 real change made, 1 to missing)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(2 real changes made, 2 to missing)
(0 real changes made)
(4 real changes made, 4 to missing)
(4 real changes made, 4 to missing)
(0 real changes made)
(1 real change made, 1 to missing)
(4 real changes made, 4 to missing)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(1 real change made, 1 to missing)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(1 real change made, 1 to missing)
(1 real change made, 1 to missing)
(2 real changes made, 2 to missing)
(1 real change made, 1 to missing)
(1 real change made, 1 to missing)
(0 real changes made)
(1 real change made, 1 to missing)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(3 real changes made, 3 to missing)
(0 real changes made)
(0 real changes made)
(2 real changes made, 2 to missing)
(0 real changes made)
(2 real changes made, 2 to missing)
(0 real changes made)
(1 real change made, 1 to missing)
(1 real change made, 1 to missing)
(1 real change made, 1 to missing)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(119 real changes made, 119 to missing)
(1 real change made, 1 to missing)
(0 real changes made)
(3 real changes made, 3 to missing)
(0 real changes made)
(0 real changes made)

{com}. 
. foreach y in `yesno'{c -(}
{txt}  2{com}. replace `y'=0  if `y'==2
{txt}  3{com}. {c )-}
{txt}(1 real change made)
(212 real changes made)
(2 real changes made)

{com}. 
. foreach y in `missingzero'{c -(}
{txt}  2{com}. replace `y'=0  if `y'==.
{txt}  3{com}. {c )-}
{txt}(55 real changes made)
(109 real changes made)
(230 real changes made)
(22 real changes made)
(195 real changes made)
(227 real changes made)
(221 real changes made)
(204 real changes made)
(9 real changes made)

{com}. 
. gen hhmember = a3
{txt}
{com}. gen hhheadage = am1_3a if am1_4 == 1
{txt}(2 missing values generated)

{com}. replace hhheadage = am2_3a if am2_4 == 1
{txt}(0 real changes made)

{com}. replace hhheadage = am3_3a if am3_4 == 1
{txt}(2 real changes made)

{com}. replace hhheadage = am4_3a if am4_4 == 1
{txt}(0 real changes made)

{com}. gen hhheadedu = am1_6 if am1_4 == 1
{txt}(2 missing values generated)

{com}. replace hhheadedu = am2_6 if am2_4 == 1
{txt}(0 real changes made)

{com}. replace hhheadedu = am3_6 if am3_4 == 1
{txt}(1 real change made)

{com}. replace hhheadedu = am4_6 if am4_4 == 1
{txt}(0 real changes made)

{com}. 
. gen hhheadeduyear = hhheadedu
{txt}(1 missing value generated)

{com}. replace hhheadeduyear = 10 if hhheadedu == 11
{txt}(1 real change made)

{com}. replace hhheadeduyear = 0 if hhheadedu == 17
{txt}(51 real changes made)

{com}. replace hhheadeduyear = 18 if hhheadedu == 15
{txt}(1 real change made)

{com}. replace hhheadeduyear = . if hhheadedu == 888
{txt}(2 real changes made, 2 to missing)

{com}. replace hhheadeduyear = . if hhheadedu == 999
{txt}(1 real change made, 1 to missing)

{com}. 
. destring x1d x1f x1h , replace
{txt}x1d: all characters numeric; {res}replaced {txt}as {res}int
{txt}x1f: all characters numeric; {res}replaced {txt}as {res}long
{txt}(167 missing values generated)
{res}{txt}x1h: all characters numeric; {res}replaced {txt}as {res}int
{txt}(228 missing values generated)
{res}{txt}
{com}. 
. keep x1d x1f x1h hhmember hhheadage hhheadedu hhheadeduyear
{txt}
{com}. 
. preserve
{txt}
{com}. collapse (mean) hhmember hhheadage hhheadedu hhheadeduyear, by(x1d)
{res}{txt}
{com}. replace hhheadeduyear = . if hhheadeduyear == 4.5
{txt}(1 real change made, 1 to missing)

{com}. rename x1d student_no
{res}{txt}
{com}. save "$path_data/temp/endline_followup_parents_data_1stchild", replace
{txt}{p 0 4 2}
file {bf}
/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/data/temp/endline_followup_parents_data_1stchild.dta{rm}
saved
{p_end}

{com}. 
. restore
{txt}
{com}. preserve
{txt}
{com}. rename x1f student_no
{res}{txt}
{com}. drop if student_no == .
{txt}(167 observations deleted)

{com}. collapse (mean) hhmember hhheadage hhheadedu hhheadeduyear, by(student_no)
{res}{txt}
{com}. replace hhheadeduyear = . if hhheadeduyear == 4.5
{txt}(1 real change made, 1 to missing)

{com}. save "$path_data/temp/endline_followup_parents_data_2ndchild", replace
{txt}{p 0 4 2}
file {bf}
/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/data/temp/endline_followup_parents_data_2ndchild.dta{rm}
saved
{p_end}

{com}. 
. restore
{txt}
{com}. preserve
{txt}
{com}. rename x1h student_no
{res}{txt}
{com}. drop if student_no == .
{txt}(228 observations deleted)

{com}. collapse (mean) hhmember hhheadage hhheadedu hhheadeduyear, by(student_no)
{res}{txt}
{com}. replace hhheadeduyear = . if hhheadeduyear == 4.5
{txt}(0 real changes made)

{com}. save "$path_data/temp/endline_followup_parents_data_3rdchild", replace
{txt}{p 0 4 2}
file {bf}
/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/data/temp/endline_followup_parents_data_3rdchild.dta{rm}
saved
{p_end}

{com}. 
. 
. 
{txt}end of do-file

{com}. 
. do "$path_do/1_data_cleaning_merge.do"
{txt}
{com}. set more off
{txt}
{com}. clear all
{res}{txt}
{com}. 
. use "$path_data/temp/followup_student_baseline_score_missing_dummy", clear
{txt}
{com}. merge 1:1 student_no using "$path_data/temp/endline_followup_parents_data_1stchild"
{res}
{txt}{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}             789
{txt}{col 9}from master{col 30}{res}             783{txt}  (_merge==1)
{col 9}from using{col 30}{res}               6{txt}  (_merge==2)

{col 5}Matched{col 30}{res}             222{txt}  (_merge==3)
{col 5}{hline 41}

{com}. rename _merge _merge_1st
{res}{txt}
{com}. rename hhmember hhmember_1st
{res}{txt}
{com}. rename hhheadage hhheadage_1st
{res}{txt}
{com}. rename hhheadeduyear hhheadeduyear_1st
{res}{txt}
{com}. 
. merge 1:1 student_no using "$path_data/temp/endline_followup_parents_data_2ndchild"
{res}{txt}{p 0 7 2}
(variable
{bf:student_no} was {bf:float}, now {bf:double} to accommodate using data's values)
{p_end}

{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}           1,025
{txt}{col 9}from master{col 30}{res}             987{txt}  (_merge==1)
{col 9}from using{col 30}{res}              38{txt}  (_merge==2)

{col 5}Matched{col 30}{res}              24{txt}  (_merge==3)
{col 5}{hline 41}

{com}. rename _merge _merge_2nd
{res}{txt}
{com}. rename hhmember hhmember_2nd
{res}{txt}
{com}. rename hhheadage hhheadage_2nd
{res}{txt}
{com}. rename hhheadeduyear hhheadeduyear_2nd
{res}{txt}
{com}. 
. merge 1:1 student_no using "$path_data/temp/endline_followup_parents_data_3rdchild"
{res}
{txt}{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}           1,047
{txt}{col 9}from master{col 30}{res}           1,047{txt}  (_merge==1)
{col 9}from using{col 30}{res}               0{txt}  (_merge==2)

{col 5}Matched{col 30}{res}               2{txt}  (_merge==3)
{col 5}{hline 41}

{com}. rename _merge _merge_3rd
{res}{txt}
{com}. rename hhmember hhmember_3rd
{res}{txt}
{com}. rename hhheadage hhheadage_3rd
{res}{txt}
{com}. rename hhheadeduyear hhheadeduyear_3rd
{res}{txt}
{com}. 
. recode hhmember* hhheadage* hhheadeduyear* (. = 0) 
{txt}(821 changes made to {bf:hhmember_1st})
(987 changes made to {bf:hhmember_2nd})
(1,047 changes made to {bf:hhmember_3rd})
(822 changes made to {bf:hhheadage_1st})
(988 changes made to {bf:hhheadage_2nd})
(1,047 changes made to {bf:hhheadage_3rd})
(826 changes made to {bf:hhheadeduyear_1st})
(991 changes made to {bf:hhheadeduyear_2nd})
(1,047 changes made to {bf:hhheadeduyear_3rd})

{com}. 
. gen hhmember = hhmember_1st + hhmember_2nd + hhmember_3rd
{txt}
{com}. gen hhheadage = hhheadage_1st + hhheadage_2nd + hhheadage_3rd
{txt}
{com}. gen hhheadeduyear = hhheadeduyear_1st + hhheadeduyear_2nd + hhheadeduyear_3rd
{txt}
{com}. 
. keep if attrition == 0
{txt}(806 observations deleted)

{com}. 
. save "$path_data/temp/followup_student_parents_matched", replace
{txt}{p 0 4 2}
file {bf}
/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/data/temp/followup_student_parents_matched.dta{rm}
saved
{p_end}

{com}. 
{txt}end of do-file

{com}. 
. *** run the codes for outputs.
. 
. 
. do "$path_do/2_table_1.do"
{txt}
{com}. * This is the do file to create "Table 1. Summary Statistics"
. set seed 123
{txt}
{com}. 
. use "$path_data/temp/followup_student_parents_matched", clear
{txt}
{com}. 
. corr rosen_pre_std cpcs_pre_std
{txt}(obs=243)

             {c |} rosen_~d cpcs_p~d
{hline 13}{c +}{hline 18}
rosen_pre_~d {c |}{res}   1.0000
{txt}cpcs_pre_std {c |}{res}   0.9026   1.0000

{txt}
{com}. corr RSES_std CPCS_std
{txt}(obs=236)

             {c |} RSES_std CPCS_std
{hline 13}{c +}{hline 18}
    RSES_std {c |}{res}   1.0000
    {txt}CPCS_std {c |}{res}   0.9701   1.0000

{txt}
{com}. 
. 
. /// Varable Selection
> /// Baseline
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat DT_score_pre_std rosen_pre_std cpcs_pre_std if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_bl = r(StatTotal)
{txt}  5{com}. 
. tabstat DT_score_pre_std rosen_pre_std cpcs_pre_std if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_bl = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}      144       145       145
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   DT_score_p~d  rosen_pre_~d  cpcs_pre_std
N {res}          144           145           145
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}       95        98        98
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   DT_score_p~d  rosen_pre_~d  cpcs_pre_std
N {res}           95            98            98
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res}-.0313509  .0382918  .1345164
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      DT_score_p~d  rosen_pre_~d  cpcs_pre_std
Mean {res}   -.03135095     .03829184      .1345164
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .0475214 -.0566567 -.1990291
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      DT_score_p~d  rosen_pre_~d  cpcs_pre_std
Mean {res}    .04752144    -.05665667    -.19902912
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} 1.023177  .9748496  .9271749
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    DT_score_p~d  rosen_pre_~d  cpcs_pre_std
SD {res}    1.0231772     .97484957     .92717486
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} .9672202  1.038561  1.073121
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    DT_score_p~d  rosen_pre_~d  cpcs_pre_std
SD {res}    .96722024      1.038561     1.0731214
{reset}
{com}. 
. matrix n_bl = J(1,3,.)
{txt}
{com}. forvalues i = 1/3 {c -(}
{txt}  2{com}.         matrix n_bl[1,`i'] = n_tr_bl[1,`i'] + n_ct_bl[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in DT_score_pre_std rosen_pre_std cpcs_pre_std{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}239
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 32
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  2
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.5
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        DT_score_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0788724{col 38}{space 1}  -0.38{col 46}{space 3}0.726{col 54}{space 3}-.5170202{col 66}{space 3} .3849625
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}           rosen_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0949485{col 38}{space 1}   0.47{col 46}{space 3}0.600{col 54}{space 3}-.3528281{col 66}{space 3} .5243389
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}            cpcs_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3335455{col 38}{space 1}   1.82{col 46}{space 3}0.116{col 54}{space 3}-.0763347{col 66}{space 3} .7082129
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. /// Family
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat hhmember hhheadage hhheadeduyear if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_parent = r(StatTotal)
{txt}  5{com}. 
. tabstat hhmember hhheadage hhheadeduyear if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_parent = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}      145       145       145
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
       hhmember     hhheadage  hhheadeduy~r
N {res}          145           145           145
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}       98        98        98
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
       hhmember     hhheadage  hhheadeduy~r
N {res}           98            98            98
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res} 4.510345  46.57241  2.331034
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
          hhmember     hhheadage  hhheadeduy~r
Mean {res}    4.5103448     46.572414     2.3310345
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res} 4.265306  46.68878  3.163265
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
          hhmember     hhheadage  hhheadeduy~r
Mean {res}    4.2653061     46.688776     3.1632653
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} 1.280827   9.03907  2.995495
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
        hhmember     hhheadage  hhheadeduy~r
SD {res}    1.2808268     9.0390702     2.9954947
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} 1.197515  9.408681  3.530993
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
        hhmember     hhheadage  hhheadeduy~r
SD {res}    1.1975148     9.4086808     3.5309935
{reset}
{com}. 
. matrix n_parent = J(1,3,.)
{txt}
{com}. forvalues i = 1/3 {c -(}
{txt}  2{com}.         matrix n_parent[1,`i'] = n_tr_parent[1,`i'] + n_ct_parent[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in hhmember hhheadage hhheadeduyear{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                hhmember{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2450387{col 38}{space 1}   1.29{col 46}{space 3}0.190{col 54}{space 3}-.1378142{col 66}{space 3} .7032024
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}               hhheadage{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.1163617{col 38}{space 1}  -0.07{col 46}{space 3}0.914{col 54}{space 3}-3.252753{col 66}{space 3} 3.440111
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}           hhheadeduyear{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.8322308{col 38}{space 1}  -2.22{col 46}{space 3}0.018{col 54}{space 3}-1.589777{col 66}{space 3}-.0728916
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. 
. 
. /// School　attendance
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat q2a q2b q2c q2h if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_school = r(StatTotal)
{txt}  5{com}. 
. tabstat q2a q2b q2c q2h if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_school = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:N} {...}
{c |}{...}
 {res}      145       145       145       145
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
   q2a  q2b  q2c  q2h
N {res} 145  145  145  145
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:N} {...}
{c |}{...}
 {res}       98        98        98        98
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
   q2a  q2b  q2c  q2h
N {res}  98   98   98   98
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .5517241  9.606897   .062069  .3793103
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
            q2a        q2b        q2c        q2h
Mean {res} .55172414  9.6068966  .06206897  .37931034
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .5306122  9.602041  .0408163  .4489796
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
            q2a        q2b        q2c        q2h
Mean {res} .53061224  9.6020408  .04081633  .44897959
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:SD} {...}
{c |}{...}
 {res} .4990412  1.029405  .2421171  .4868973
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
          q2a        q2b        q2c        q2h
SD {res} .49904123  1.0294048   .2421171  .48689728
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:SD} {...}
{c |}{...}
 {res} .5016279  .8703571  .1988818  .4999474
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
          q2a        q2b        q2c        q2h
SD {res}  .5016279  .87035715  .19888179   .4999474
{reset}
{com}. 
. matrix n_school = J(1,4,.)
{txt}
{com}. forvalues i = 1/4 {c -(}
{txt}  2{com}.         matrix n_school[1,`i'] = n_tr_school[1,`i'] + n_ct_school[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in q2a q2b q2c q2h{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                     q2a{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0211119{col 38}{space 1}   0.25{col 46}{space 3}0.868{col 54}{space 3}-.1592541{col 66}{space 3} .2083223
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                     q2b{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0048557{col 38}{space 1}   0.03{col 46}{space 3}0.990{col 54}{space 3}-.3899922{col 66}{space 3}  .336345
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                     q2c{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0212526{col 38}{space 1}   0.56{col 46}{space 3}0.662{col 54}{space 3}-.0533022{col 66}{space 3} .0982309
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                     q2h{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0696692{col 38}{space 1}  -0.85{col 46}{space 3}0.446{col 54}{space 3}-.2494278{col 66}{space 3} .1087737
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. /// Other study variable
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat tutor study_other study_affect_covid hometutoring onlineclass studymyself parentsteach if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_study = r(StatTotal)
{txt}  5{com}. 
. tabstat tutor study_other study_affect_covid hometutoring onlineclass studymyself parentsteach if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_study = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:N} {...}
{c |}{...}
 {res}      145       145       145       145       145       145       145
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
          tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
N {res}          145           145           145           145           145           145           145
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:N} {...}
{c |}{...}
 {res}       98        98        98        98        98        98        98
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
          tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
N {res}           98            98            98            98            98            98            98
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:Mean} {...}
{c |}{...}
 {res}  .337931   .462069  .6482759  .0965517  .0482759  .5241379  .0275862
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
             tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
Mean {res}    .33793103     .46206897     .64827586     .09655172     .04827586     .52413793     .02758621
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .4591837  .4285714  .6020408  .1428571  .1326531  .5306122  .1938776
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
             tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
Mean {res}    .45918367     .42857143     .60204082     .14285714     .13265306     .53061224     .19387755
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:SD} {...}
{c |}{...}
 {res} .4746445  .5002873  .4791635  .2963701  .2150915  .5011481  .1643517
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
           tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
SD {res}    .47464445     .50028727     .47916354     .29637012     .21509153     .50114811     .16435174
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:SD} {...}
{c |}{...}
 {res} .5008934   .497416  .4919935  .3517262  .3409434  .5016279  .3973667
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
           tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
SD {res}    .50089337       .497416     .49199354     .35172623     .34094336      .5016279     .39736667
{reset}
{com}. 
. matrix n_study = J(1,8,.)
{txt}
{com}. forvalues i = 1/8 {c -(}
{txt}  2{com}.         matrix n_study[1,`i'] = n_tr_study[1,`i'] + n_ct_study[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in tutor study_other study_affect_covid hometutoring onlineclass studymyself parentsteach{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                   tutor{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.1212526{col 38}{space 1}  -1.69{col 46}{space 3}0.120{col 54}{space 3}-.2773205{col 66}{space 3} .0421174
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}             study_other{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0334975{col 38}{space 1}   0.39{col 46}{space 3}0.742{col 54}{space 3}-.1488081{col 66}{space 3} .2186319
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}      study_affect_covid{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  .046235{col 38}{space 1}   0.56{col 46}{space 3}0.576{col 54}{space 3} -.124707{col 66}{space 3} .2364771
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}            hometutoring{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0463054{col 38}{space 1}  -1.11{col 46}{space 3}0.312{col 54}{space 3} -.128302{col 66}{space 3} .0430425
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}             onlineclass{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0843772{col 38}{space 1}  -1.92{col 46}{space 3}0.096{col 54}{space 3} -.179885{col 66}{space 3} .0148069
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}             studymyself{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0064743{col 38}{space 1}  -0.08{col 46}{space 3}0.940{col 54}{space 3}-.1754887{col 66}{space 3} .1699468
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}            parentsteach{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.1662913{col 38}{space 1}  -3.85{col 46}{space 3}0.000{col 54}{space 3} -.255542{col 66}{space 3}-.0629773
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. /// Cognitive
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat followup_cog_std if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_cog = r(StatTotal)
{txt}  5{com}. 
. tabstat followup_cog_std if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_cog = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}{ralign 12:Variable} {...}
{c |}         N
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res}      145
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
   followup_c~d
N {res}          145
{reset}
{ralign 12:Variable} {...}
{c |}         N
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res}       98
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
   followup_c~d
N {res}           98
{reset}
{ralign 12:Variable} {...}
{c |}      Mean
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res}-.0920409
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
      followup_c~d
Mean {res}   -.09204085
{reset}
{ralign 12:Variable} {...}
{c |}      Mean
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res} .1361831
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
      followup_c~d
Mean {res}    .13618309
{reset}
{ralign 12:Variable} {...}
{c |}        SD
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res} 1.070796
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
    followup_c~d
SD {res}     1.070796
{reset}
{ralign 12:Variable} {...}
{c |}        SD
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res} .8725076
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
    followup_c~d
SD {res}    .87250763
{reset}
{com}. 
. matrix n_cog = J(1,1,.)
{txt}
{com}. forvalues i = 1/1 {c -(}
{txt}  2{com}.         matrix n_cog[1,`i'] = n_tr_cog[1,`i'] + n_ct_cog[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2282239{col 38}{space 1}  -1.36{col 46}{space 3}0.192{col 54}{space 3}-.5930653{col 66}{space 3} .1293723
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}.         
. matrix r2_followup_cog_std_temp = r(table)
{txt}
{com}. 
. 
. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix r2_followup_cog_std_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix r2_followup_cog_std_mean[1,`j'] = r2_followup_cog_std_temp[1,`j']
{txt}  3{com}. * standard error
. * matrix r2_followup_cog_std_se[1,`j'] = r2_followup_cog_std_temp[2,`j']
. * p value
. matrix r2_followup_cog_std_pv[1,`j'] = r2_followup_cog_std_temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}.     
. /// Non cognitive
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat followup_noncog_std RSES_std CPCS_std if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_noncog = r(StatTotal)
{txt}  5{com}. 
. tabstat followup_noncog_std RSES_std CPCS_std if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_noncog = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}      105       140       140
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   followup_n~d      RSES_std      CPCS_std
N {res}          105           140           140
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}       74        96        96
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   followup_n~d      RSES_std      CPCS_std
N {res}           74            96            96
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .1969319  .1591241  .1745941
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      followup_n~d      RSES_std      CPCS_std
Mean {res}    .19693189      .1591241     .17459415
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res}-.2794302 -.2320565  -.254617
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      followup_n~d      RSES_std      CPCS_std
Mean {res}   -.27943024    -.23205648    -.25461705
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} 1.006158  1.022691  1.008304
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    followup_n~d      RSES_std      CPCS_std
SD {res}    1.0061577     1.0226907     1.0083041
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} .9279901  .9228443  .9357831
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    followup_n~d      RSES_std      CPCS_std
SD {res}    .92799012     .92284427     .93578307
{reset}
{com}. 
. matrix n_noncog = J(1,3,.)
{txt}
{com}. forvalues i = 1/3 {c -(}
{txt}  2{com}.         matrix n_noncog[1,`i'] = n_tr_noncog[1,`i'] + n_ct_noncog[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in followup_noncog_std RSES_std CPCS_std{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}179
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 32
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}5.6
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}     followup_noncog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .4763621{col 38}{space 1}   2.08{col 46}{space 3}0.074{col 54}{space 3}-.0489589{col 66}{space 3} .9729653
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}236
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.2
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3911806{col 38}{space 1}   2.02{col 46}{space 3}0.064{col 54}{space 3}-.0321475{col 66}{space 3} .7806997
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}236
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.2
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .4292112{col 38}{space 1}   2.26{col 46}{space 3}0.038{col 54}{space 3} .0238533{col 66}{space 3} .7985476
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. 
. /// Behavioral
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat hyper if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_hyper = r(StatTotal)
{txt}  5{com}. 
. tabstat hyper if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_hyper = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}{ralign 12:Variable} {...}
{c |}         N
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res}      113
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
   hyper
N {res}   113
{reset}
{ralign 12:Variable} {...}
{c |}         N
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res}       71
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
   hyper
N {res}    71
{reset}
{ralign 12:Variable} {...}
{c |}      Mean
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res} .2654867
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
          hyper
Mean {res} .26548673
{reset}
{ralign 12:Variable} {...}
{c |}      Mean
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res} .0704225
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
          hyper
Mean {res} .07042254
{reset}
{ralign 12:Variable} {...}
{c |}        SD
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res}  .443559
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
        hyper
SD {res} .44355905
{reset}
{ralign 12:Variable} {...}
{c |}        SD
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res} .2576789
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
        hyper
SD {res} .25767885
{reset}
{com}. 
. matrix n_hyper = J(1,1,.)
{txt}
{com}. forvalues i = 1/1 {c -(}
{txt}  2{com}.         matrix n_hyper[1,`i'] = n_tr_hyper[1,`i'] + n_ct_hyper[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in hyper{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment if hypernoinfo == 0, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}184
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}5.6
{col 69}{txt}max{col 72} = {res} 12
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                   hyper{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1950642{col 38}{space 1}   3.37{col 46}{space 3}0.010{col 54}{space 3} .0645109{col 66}{space 3} .3241339
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. 
. // significant level
. 
. local outcome DT_score_pre_std rosen_pre_std cpcs_pre_std hhmember hhheadage hhheadeduyear q2a q2c q2h tutor study_other followup_cog_std RSES_std CPCS_std
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}.                 if r2_`dep'_pv[1,1]<=0.01 {c -(}
{txt}  3{com}.                         local star_`dep' %3s "***"
{txt}  4{com}.                 {c )-}
{txt}  5{com}.                 else if (r2_`dep'_pv[1,1]>0.01) & (r2_`dep'_pv[1,1]<=0.05) {c -(}
{txt}  6{com}.                         local star_`dep' %2s "**"
{txt}  7{com}.                 {c )-}
{txt}  8{com}.                 else if (r2_`dep'_pv[1,1]>0.05) & (r2_`dep'_pv[1,1]<=0.10) {c -(}
{txt}  9{com}.                         local star_`dep' %1s "*"
{txt} 10{com}.                 {c )-}
{txt} 11{com}.                 else {c -(}
{txt} 12{com}.                         local star_`dep'  ""
{txt} 13{com}.                 {c )-}
{txt} 14{com}. {c )-} 
{txt}
{com}. 
. set seed 1
{txt}
{com}. rwolf DT_score_pre_std rosen_pre_std cpcs_pre_std hhmember hhheadage hhheadeduyear, indepvar(treatment) reps(1000)
Bootstrap replications (1000). This may take some time.
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Romano-Wolf step-down adjusted p-values


Independent variable:  treatment
Outcome variables:   DT_score_pre_std rosen_pre_std cpcs_pre_std hhmember
{col 22}hhheadage hhheadeduyear
Number of resamples: 1000


{hline 78}
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
{hline 20}+{hline 57}
   {txt}DT_score_pre_std {c |}    {res}0.5518             0.5495              0.8312
      {txt}rosen_pre_std {c |}    {res}0.4689             0.4496              0.8312
       {txt}cpcs_pre_std {c |}    {res}0.0105             0.0160              0.0589
           {txt}hhmember {c |}    {res}0.1345             0.1469              0.4635
          {txt}hhheadage {c |}    {res}0.9229             0.9201              0.9201
      {txt}hhheadeduyear {c |}    {res}0.0494             0.0500              0.2478
{hline 78}
{txt}
{com}. set seed 1
{txt}
{com}. rwolf q2a q2c q2h tutor study_other followup_cog_std RSES_std CPCS_std, indepvar(treatment) reps(1000)
Bootstrap replications (1000). This may take some time.
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Romano-Wolf step-down adjusted p-values


Independent variable:  treatment
Outcome variables:   q2a q2c q2h tutor study_other followup_cog_std RSES_std CPCS_std
Number of resamples: 1000


{hline 78}
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
{hline 20}+{hline 57}
                {txt}q2a {c |}    {res}0.7471             0.7493              0.7872
                {txt}q2c {c |}    {res}0.4722             0.4595              0.7872
                {txt}q2h {c |}    {res}0.2801             0.2717              0.5764
              {txt}tutor {c |}    {res}0.0573             0.0460              0.2298
        {txt}study_other {c |}    {res}0.6083             0.6234              0.7872
   {txt}followup_cog_std {c |}    {res}0.0809             0.0799              0.2697
           {txt}RSES_std {c |}    {res}0.0030             0.0030              0.0110
           {txt}CPCS_std {c |}    {res}0.0011             0.0020              0.0060
{hline 78}
{txt}
{com}. 
. 
. /// Table
> tempname hh2
{txt}
{com}. file open `hh2' using "$path_output/summary_stat.tex", write replace
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "% Author: Kazuma Takakura" _newline
{txt}
{com}. file write `hh2' "% Date: `c(current_date)'" _newline
{txt}
{com}. file write `hh2' "% Time: `c(current_time)'" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. 
. file write `hh2' "\begin{c -(}table{c )-}[h!]\footnotesize" _newline
{txt}
{com}. file write `hh2' "  \centering" _newline
{txt}
{com}. file write `hh2' "  \caption{c -(}Summary Statistics{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:sumstat{c )-}" _newline
{txt}
{com}. file write `hh2' "\scalebox{c -(}1{c )-}{c -(}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}threeparttable{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "\begin{c -(}tabular{c )-}{c -(}lccccc{c )-}\toprule" _newline
{txt}
{com}. 
.   
. file write `hh2' " Dependent Variable & Treatment &  Control  & Difference & N   \\\midrule\midrule" _newline
{txt}
{com}. file write `hh2' " Panel A: Baseline & & & &   \\ " _newline
{txt}
{com}. file write `hh2' " DT score^{c -(}a{c )-} & " %04.3f (mean_tr_bl[1,1]) " & " %04.3f (mean_ct_bl[1,1]) " & " %04.3f (r2_DT_score_pre_std_mean[1,1]) `star_DT_score_pre_std' " & " (n_bl[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_bl[1,1]) " ] & [ " %04.3f (sd_ct_bl[1,1]) " ] & ( " %04.3f (r2_DT_score_pre_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.831) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. 
. file write `hh2' " RSES score^{c -(}a{c )-} & " %04.3f (mean_tr_bl[1,2]) " & " %04.3f (mean_ct_bl[1,2]) " & " %04.3f (r2_rosen_pre_std_mean[1,1]) `star_rosen_pre_std' " & "  (n_bl[1,2]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_bl[1,2]) " ] & [ " %04.3f (sd_ct_bl[1,2]) " ] & ( " %04.3f (r2_rosen_pre_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.831) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " CPCS score^{c -(}a{c )-} & " %04.3f (mean_tr_bl[1,3]) " & " %04.3f (mean_ct_bl[1,3]) " & " %04.3f (r2_cpcs_pre_std_mean[1,1]) `star_cpcs_pre_std' " & "  (n_bl[1,3]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_bl[1,3]) " ] & [ " %04.3f (sd_ct_bl[1,3]) " ] & ( " %04.3f (r2_cpcs_pre_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.059) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Household size & " %04.3f (mean_tr_parent[1,1]) " & " %04.3f (mean_ct_parent[1,1]) " & " %04.3f (r2_hhmember_mean[1,1]) `star_hhmember'  " & " (n_parent[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_parent[1,1]) " ] & [ " %04.3f (sd_ct_parent[1,1]) " ] & ( " %04.3f (r2_hhmember_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.464) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Household head age & " %04.3f (mean_tr_parent[1,2]) " & " %04.3f (mean_ct_parent[1,2]) " & " %04.3f (r2_hhheadage_mean[1,1]) `star_hhheadage' " & "  (n_parent[1,2]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_parent[1,2]) " ] & [ " %04.3f (sd_ct_parent[1,2]) " ] & ( " %04.3f (r2_hhheadage_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.920) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Household head education & " %04.3f (mean_tr_parent[1,3]) " & " %04.3f (mean_ct_parent[1,3]) " & " %04.3f (r2_hhheadeduyear_mean[1,1]) `star_hhheadeduyear' " & "  (n_parent[1,3]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_parent[1,3]) " ] & [ " %04.3f (sd_ct_parent[1,3]) " ] & ( " %04.3f (r2_hhheadeduyear_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.248) " \{c )-} &   \\ " _newline
{txt}
{com}. file write `hh2' " \\ "_newline
{txt}
{com}. 
. file write `hh2' " Panel B: Follow-up & & & &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " School attendance & " %04.3f (mean_tr_school[1,1]) " & " %04.3f (mean_ct_school[1,1]) " & " %04.3f (r2_q2a_mean[1,1]) `star_q2a' " & " (n_school[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_school[1,1]) " ] & [ " %04.3f (sd_ct_school[1,1]) " ] & ( " %04.3f (r2_q2a_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.787) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Grade repeat & " %04.3f (mean_tr_school[1,3]) " & " %04.3f (mean_ct_school[1,3]) " & " %04.3f (r2_q2c_mean[1,1]) `star_q2c' " & "  (n_school[1,3]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_school[1,3]) " ] & [ " %04.3f (sd_ct_school[1,3]) " ] & ( " %04.3f (r2_q2c_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.787) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Drop out & " %04.3f (mean_tr_school[1,4]) " & " %04.3f (mean_ct_school[1,4]) " & " %04.3f (r2_q2h_mean[1,1]) `star_q2h'  " & "  (n_school[1,4]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_school[1,4]) " ] & [ " %04.3f (sd_ct_school[1,4]) " ] & ( " %04.3f (r2_q2h_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.576) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Tutoring & " %04.3f (mean_tr_study[1,1]) " & " %04.3f (mean_ct_study[1,1]) " & " %04.3f (r2_tutor_mean[1,1]) `star_tutor'  " & " (n_study[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_study[1,1]) " ] & [ " %04.3f (sd_ct_study[1,1]) " ] & ( " %04.3f (r2_tutor_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.230) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Self-study & " %04.3f (mean_tr_study[1,2]) " & " %04.3f (mean_ct_study[1,2]) " & " %04.3f (r2_study_other_mean[1,1]) `star_study_other' " & "  (n_study[1,2]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_study[1,2]) " ] & [ " %04.3f (sd_ct_study[1,2]) " ] & ( " %04.3f (r2_study_other_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.787) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Rapid math test score^{c -(}a{c )-} & " %04.3f (mean_tr_cog[1,1]) " & " %04.3f (mean_ct_cog[1,1]) " & " %04.3f (r2_followup_cog_std_mean[1,1]) `star_followup_cog_std'  "  & "  (n_cog[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_cog[1,1]) " ] & [ " %04.3f (sd_ct_cog[1,1]) " ] & ( " %04.3f (r2_followup_cog_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.270) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " RSES score^{c -(}a{c )-} & " %04.3f (mean_tr_noncog[1,2]) " & " %04.3f (mean_ct_noncog[1,2]) " & " %04.3f (r2_RSES_std_mean[1,1])   `star_RSES_std' " & " (n_noncog[1,2]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_noncog[1,2]) " ] & [ " %04.3f (sd_ct_noncog[1,2]) " ] & ( " %04.3f (r2_RSES_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.011) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " CPCS score^{c -(}a{c )-} & " %04.3f (mean_tr_noncog[1,3]) " & " %04.3f (mean_ct_noncog[1,3]) " & " %04.3f (r2_CPCS_std_mean[1,1])   `star_CPCS_std' "&  " (n_noncog[1,3]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_noncog[1,3]) " ] & [ " %04.3f (sd_ct_noncog[1,3]) " ] & ( " %04.3f (r2_CPCS_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.006) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. 
. file write `hh2' "\midrule" _newline
{txt}
{com}. file write `hh2' "\end{c -(}tabular{c )-}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\item (a) Variables are standardized using the average and variance of the whole follow-up sample in the February 2022 survey. " _newline
{txt}
{com}. file write `hh2' "\item (b) Standard deviations are reported in square brackets.  " _newline
{txt}
{com}. file write `hh2' "\item (c) Wild clustered bootstrap p-values are reported within parentheses. Clusters are schools at the baseline. There are 34 clusters. " _newline
{txt}
{com}. file write `hh2' "\item (d) Romano-Wolf multiple hypothesis testing p-values are reported in curly brackets. This test is conducted separately for the baseline variables and the follow-up variables." _newline
{txt}
{com}. file write `hh2' "\item (e) Statistical significance is indicated by stars based on the wild clustered bootstrap p-values reported in parentheses: $*$ denotes significance at the 10\% level, $∗∗$ at the 5\% level, and $∗∗∗$ at the 1\% level.  " _newline
{txt}
{com}. file write `hh2' "\end{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}threeparttable{c )-}" _newline
{txt}
{com}. file write `hh2' "{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}table{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. file close `hh2'
{txt}
{com}. 
{txt}end of do-file

{com}. 
{txt}end of do-file

{com}. do "/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/do_files/2_table_1.do"
{txt}
{com}. * This is the do file to create "Table 1. Summary Statistics"
. set seed 1
{txt}
{com}. 
. use "$path_data/temp/followup_student_parents_matched", clear
{txt}
{com}. 
. corr rosen_pre_std cpcs_pre_std
{txt}(obs=243)

             {c |} rosen_~d cpcs_p~d
{hline 13}{c +}{hline 18}
rosen_pre_~d {c |}{res}   1.0000
{txt}cpcs_pre_std {c |}{res}   0.9026   1.0000

{txt}
{com}. corr RSES_std CPCS_std
{txt}(obs=236)

             {c |} RSES_std CPCS_std
{hline 13}{c +}{hline 18}
    RSES_std {c |}{res}   1.0000
    {txt}CPCS_std {c |}{res}   0.9701   1.0000

{txt}
{com}. 
. 
. /// Varable Selection
> /// Baseline
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat DT_score_pre_std rosen_pre_std cpcs_pre_std if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_bl = r(StatTotal)
{txt}  5{com}. 
. tabstat DT_score_pre_std rosen_pre_std cpcs_pre_std if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_bl = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}      144       145       145
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   DT_score_p~d  rosen_pre_~d  cpcs_pre_std
N {res}          144           145           145
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}       95        98        98
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   DT_score_p~d  rosen_pre_~d  cpcs_pre_std
N {res}           95            98            98
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res}-.0313509  .0382918  .1345164
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      DT_score_p~d  rosen_pre_~d  cpcs_pre_std
Mean {res}   -.03135095     .03829184      .1345164
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .0475214 -.0566567 -.1990291
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      DT_score_p~d  rosen_pre_~d  cpcs_pre_std
Mean {res}    .04752144    -.05665667    -.19902912
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} 1.023177  .9748496  .9271749
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    DT_score_p~d  rosen_pre_~d  cpcs_pre_std
SD {res}    1.0231772     .97484957     .92717486
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} .9672202  1.038561  1.073121
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    DT_score_p~d  rosen_pre_~d  cpcs_pre_std
SD {res}    .96722024      1.038561     1.0731214
{reset}
{com}. 
. matrix n_bl = J(1,3,.)
{txt}
{com}. forvalues i = 1/3 {c -(}
{txt}  2{com}.         matrix n_bl[1,`i'] = n_tr_bl[1,`i'] + n_ct_bl[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in DT_score_pre_std rosen_pre_std cpcs_pre_std{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(100)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}{txt}{p 0 6 2}note: for equal-tailed {bf:95}% CI, better performance is obtained when {bf:.025}*{bf:reps()} is an integer.{p_end}
{p 0 6 2}note: setting repetitions to {res}120{txt}.{p_end}

{p}Performing {res}      120{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}239
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 32
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  2
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.5
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        DT_score_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0788724{col 38}{space 1}  -0.38{col 46}{space 3}0.583{col 54}{space 3}-.5358419{col 66}{space 3} .3346485
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}{p 0 6 2}note: for equal-tailed {bf:95}% CI, better performance is obtained when {bf:.025}*{bf:reps()} is an integer.{p_end}
{p 0 6 2}note: setting repetitions to {res}120{txt}.{p_end}

{p}Performing {res}      120{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}           rosen_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0949485{col 38}{space 1}   0.47{col 46}{space 3}0.717{col 54}{space 3}-.3364974{col 66}{space 3} .5065742
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}{p 0 6 2}note: for equal-tailed {bf:95}% CI, better performance is obtained when {bf:.025}*{bf:reps()} is an integer.{p_end}
{p 0 6 2}note: setting repetitions to {res}120{txt}.{p_end}

{p}Performing {res}      120{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}            cpcs_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3335455{col 38}{space 1}   1.82{col 46}{space 3}0.100{col 54}{space 3}-.1438866{col 66}{space 3} .7656713
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. /// Family
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat hhmember hhheadage hhheadeduyear if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_parent = r(StatTotal)
{txt}  5{com}. 
. tabstat hhmember hhheadage hhheadeduyear if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_parent = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}      145       145       145
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
       hhmember     hhheadage  hhheadeduy~r
N {res}          145           145           145
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}       98        98        98
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
       hhmember     hhheadage  hhheadeduy~r
N {res}           98            98            98
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res} 4.510345  46.57241  2.331034
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
          hhmember     hhheadage  hhheadeduy~r
Mean {res}    4.5103448     46.572414     2.3310345
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res} 4.265306  46.68878  3.163265
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
          hhmember     hhheadage  hhheadeduy~r
Mean {res}    4.2653061     46.688776     3.1632653
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} 1.280827   9.03907  2.995495
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
        hhmember     hhheadage  hhheadeduy~r
SD {res}    1.2808268     9.0390702     2.9954947
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} 1.197515  9.408681  3.530993
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
        hhmember     hhheadage  hhheadeduy~r
SD {res}    1.1975148     9.4086808     3.5309935
{reset}
{com}. 
. matrix n_parent = J(1,3,.)
{txt}
{com}. forvalues i = 1/3 {c -(}
{txt}  2{com}.         matrix n_parent[1,`i'] = n_tr_parent[1,`i'] + n_ct_parent[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in hhmember hhheadage hhheadeduyear{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:19})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                hhmember{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2450387{col 38}{space 1}   1.29{col 46}{space 3}0.190{col 54}{space 3}-.1535148{col 66}{space 3} .6296271
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}               hhheadage{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.1163617{col 38}{space 1}  -0.07{col 46}{space 3}0.946{col 54}{space 3}-3.091406{col 66}{space 3} 3.318619
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}30{text}.{text}.{text} done{text} ({result:32})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}           hhheadeduyear{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.8322308{col 38}{space 1}  -2.22{col 46}{space 3}0.050{col 54}{space 3}-1.627946{col 66}{space 3} .0100702
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. 
. 
. /// School　attendance
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat q2a q2b q2c q2h if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_school = r(StatTotal)
{txt}  5{com}. 
. tabstat q2a q2b q2c q2h if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_school = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:N} {...}
{c |}{...}
 {res}      145       145       145       145
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
   q2a  q2b  q2c  q2h
N {res} 145  145  145  145
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:N} {...}
{c |}{...}
 {res}       98        98        98        98
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
   q2a  q2b  q2c  q2h
N {res}  98   98   98   98
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .5517241  9.606897   .062069  .3793103
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
            q2a        q2b        q2c        q2h
Mean {res} .55172414  9.6068966  .06206897  .37931034
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .5306122  9.602041  .0408163  .4489796
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
            q2a        q2b        q2c        q2h
Mean {res} .53061224  9.6020408  .04081633  .44897959
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:SD} {...}
{c |}{...}
 {res} .4990412  1.029405  .2421171  .4868973
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
          q2a        q2b        q2c        q2h
SD {res} .49904123  1.0294048   .2421171  .48689728
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:SD} {...}
{c |}{...}
 {res} .5016279  .8703571  .1988818  .4999474
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
          q2a        q2b        q2c        q2h
SD {res}  .5016279  .87035715  .19888179   .4999474
{reset}
{com}. 
. matrix n_school = J(1,4,.)
{txt}
{com}. forvalues i = 1/4 {c -(}
{txt}  2{com}.         matrix n_school[1,`i'] = n_tr_school[1,`i'] + n_ct_school[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in q2a q2b q2c q2h{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                     q2a{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0211119{col 38}{space 1}   0.25{col 46}{space 3}0.796{col 54}{space 3}-.1354197{col 66}{space 3} .2038273
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                     q2b{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0048557{col 38}{space 1}   0.03{col 46}{space 3}0.968{col 54}{space 3}-.3765979{col 66}{space 3} .3781743
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                     q2c{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0212526{col 38}{space 1}   0.56{col 46}{space 3}0.654{col 54}{space 3}-.0530806{col 66}{space 3} .0989271
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                     q2h{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0696692{col 38}{space 1}  -0.85{col 46}{space 3}0.412{col 54}{space 3}-.2342348{col 66}{space 3} .1022903
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. /// Other study variable
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat tutor study_other study_affect_covid hometutoring onlineclass studymyself parentsteach if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_study = r(StatTotal)
{txt}  5{com}. 
. tabstat tutor study_other study_affect_covid hometutoring onlineclass studymyself parentsteach if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_study = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:N} {...}
{c |}{...}
 {res}      145       145       145       145       145       145       145
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
          tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
N {res}          145           145           145           145           145           145           145
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:N} {...}
{c |}{...}
 {res}       98        98        98        98        98        98        98
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
          tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
N {res}           98            98            98            98            98            98            98
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:Mean} {...}
{c |}{...}
 {res}  .337931   .462069  .6482759  .0965517  .0482759  .5241379  .0275862
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
             tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
Mean {res}    .33793103     .46206897     .64827586     .09655172     .04827586     .52413793     .02758621
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .4591837  .4285714  .6020408  .1428571  .1326531  .5306122  .1938776
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
             tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
Mean {res}    .45918367     .42857143     .60204082     .14285714     .13265306     .53061224     .19387755
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:SD} {...}
{c |}{...}
 {res} .4746445  .5002873  .4791635  .2963701  .2150915  .5011481  .1643517
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
           tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
SD {res}    .47464445     .50028727     .47916354     .29637012     .21509153     .50114811     .16435174
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:SD} {...}
{c |}{...}
 {res} .5008934   .497416  .4919935  .3517262  .3409434  .5016279  .3973667
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
           tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
SD {res}    .50089337       .497416     .49199354     .35172623     .34094336      .5016279     .39736667
{reset}
{com}. 
. matrix n_study = J(1,8,.)
{txt}
{com}. forvalues i = 1/8 {c -(}
{txt}  2{com}.         matrix n_study[1,`i'] = n_tr_study[1,`i'] + n_ct_study[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in tutor study_other study_affect_covid hometutoring onlineclass studymyself parentsteach{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                   tutor{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.1212526{col 38}{space 1}  -1.69{col 46}{space 3}0.124{col 54}{space 3}-.2746828{col 66}{space 3} .0290105
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}             study_other{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0334975{col 38}{space 1}   0.39{col 46}{space 3}0.692{col 54}{space 3}-.1388099{col 66}{space 3} .2215483
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}      study_affect_covid{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  .046235{col 38}{space 1}   0.56{col 46}{space 3}0.578{col 54}{space 3}-.1141951{col 66}{space 3} .2148926
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}            hometutoring{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0463054{col 38}{space 1}  -1.11{col 46}{space 3}0.322{col 54}{space 3}-.1347077{col 66}{space 3} .0496548
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}             onlineclass{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0843772{col 38}{space 1}  -1.92{col 46}{space 3}0.090{col 54}{space 3}-.1871098{col 66}{space 3} .0133152
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}             studymyself{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0064743{col 38}{space 1}  -0.08{col 46}{space 3}0.922{col 54}{space 3}-.1790013{col 66}{space 3}  .173866
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}            parentsteach{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.1662913{col 38}{space 1}  -3.85{col 46}{space 3}0.002{col 54}{space 3}-.2634871{col 66}{space 3}-.0729065
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. /// Cognitive
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat followup_cog_std if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_cog = r(StatTotal)
{txt}  5{com}. 
. tabstat followup_cog_std if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_cog = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}{ralign 12:Variable} {...}
{c |}         N
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res}      145
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
   followup_c~d
N {res}          145
{reset}
{ralign 12:Variable} {...}
{c |}         N
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res}       98
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
   followup_c~d
N {res}           98
{reset}
{ralign 12:Variable} {...}
{c |}      Mean
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res}-.0920409
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
      followup_c~d
Mean {res}   -.09204085
{reset}
{ralign 12:Variable} {...}
{c |}      Mean
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res} .1361831
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
      followup_c~d
Mean {res}    .13618309
{reset}
{ralign 12:Variable} {...}
{c |}        SD
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res} 1.070796
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
    followup_c~d
SD {res}     1.070796
{reset}
{ralign 12:Variable} {...}
{c |}        SD
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res} .8725076
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
    followup_c~d
SD {res}    .87250763
{reset}
{com}. 
. matrix n_cog = J(1,1,.)
{txt}
{com}. forvalues i = 1/1 {c -(}
{txt}  2{com}.         matrix n_cog[1,`i'] = n_tr_cog[1,`i'] + n_ct_cog[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2282239{col 38}{space 1}  -1.36{col 46}{space 3}0.168{col 54}{space 3}-.5800146{col 66}{space 3}  .101623
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}.         
. matrix r2_followup_cog_std_temp = r(table)
{txt}
{com}. 
. 
. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix r2_followup_cog_std_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix r2_followup_cog_std_mean[1,`j'] = r2_followup_cog_std_temp[1,`j']
{txt}  3{com}. * standard error
. * matrix r2_followup_cog_std_se[1,`j'] = r2_followup_cog_std_temp[2,`j']
. * p value
. matrix r2_followup_cog_std_pv[1,`j'] = r2_followup_cog_std_temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}.     
. /// Non cognitive
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat followup_noncog_std RSES_std CPCS_std if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_noncog = r(StatTotal)
{txt}  5{com}. 
. tabstat followup_noncog_std RSES_std CPCS_std if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_noncog = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}      105       140       140
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   followup_n~d      RSES_std      CPCS_std
N {res}          105           140           140
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}       74        96        96
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   followup_n~d      RSES_std      CPCS_std
N {res}           74            96            96
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .1969319  .1591241  .1745941
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      followup_n~d      RSES_std      CPCS_std
Mean {res}    .19693189      .1591241     .17459415
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res}-.2794302 -.2320565  -.254617
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      followup_n~d      RSES_std      CPCS_std
Mean {res}   -.27943024    -.23205648    -.25461705
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} 1.006158  1.022691  1.008304
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    followup_n~d      RSES_std      CPCS_std
SD {res}    1.0061577     1.0226907     1.0083041
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} .9279901  .9228443  .9357831
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    followup_n~d      RSES_std      CPCS_std
SD {res}    .92799012     .92284427     .93578307
{reset}
{com}. 
. matrix n_noncog = J(1,3,.)
{txt}
{com}. forvalues i = 1/3 {c -(}
{txt}  2{com}.         matrix n_noncog[1,`i'] = n_tr_noncog[1,`i'] + n_ct_noncog[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in followup_noncog_std RSES_std CPCS_std{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}30{text} done{text} ({result:30})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}179
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 32
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}5.6
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}     followup_noncog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .4763621{col 38}{space 1}   2.08{col 46}{space 3}0.058{col 54}{space 3}-.0211131{col 66}{space 3} .9577882
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}236
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.2
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3911806{col 38}{space 1}   2.02{col 46}{space 3}0.056{col 54}{space 3}-.0120867{col 66}{space 3} .7999362
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}236
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.2
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .4292112{col 38}{space 1}   2.26{col 46}{space 3}0.030{col 54}{space 3} .0594576{col 66}{space 3} .8466888
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. 
. /// Behavioral
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat hyper if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_hyper = r(StatTotal)
{txt}  5{com}. 
. tabstat hyper if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_hyper = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}{ralign 12:Variable} {...}
{c |}         N
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res}      113
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
   hyper
N {res}   113
{reset}
{ralign 12:Variable} {...}
{c |}         N
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res}       71
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
   hyper
N {res}    71
{reset}
{ralign 12:Variable} {...}
{c |}      Mean
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res} .2654867
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
          hyper
Mean {res} .26548673
{reset}
{ralign 12:Variable} {...}
{c |}      Mean
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res} .0704225
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
          hyper
Mean {res} .07042254
{reset}
{ralign 12:Variable} {...}
{c |}        SD
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res}  .443559
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
        hyper
SD {res} .44355905
{reset}
{ralign 12:Variable} {...}
{c |}        SD
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res} .2576789
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
        hyper
SD {res} .25767885
{reset}
{com}. 
. matrix n_hyper = J(1,1,.)
{txt}
{com}. forvalues i = 1/1 {c -(}
{txt}  2{com}.         matrix n_hyper[1,`i'] = n_tr_hyper[1,`i'] + n_ct_hyper[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in hyper{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment if hypernoinfo == 0, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:18})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}184
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}5.6
{col 69}{txt}max{col 72} = {res} 12
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                   hyper{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1950642{col 38}{space 1}   3.37{col 46}{space 3}0.000{col 54}{space 3} .0752058{col 66}{space 3} .3240322
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. 
. // significant level
. 
. local outcome DT_score_pre_std rosen_pre_std cpcs_pre_std hhmember hhheadage hhheadeduyear q2a q2c q2h tutor study_other followup_cog_std RSES_std CPCS_std
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}.                 if r2_`dep'_pv[1,1]<=0.01 {c -(}
{txt}  3{com}.                         local star_`dep' %3s "***"
{txt}  4{com}.                 {c )-}
{txt}  5{com}.                 else if (r2_`dep'_pv[1,1]>0.01) & (r2_`dep'_pv[1,1]<=0.05) {c -(}
{txt}  6{com}.                         local star_`dep' %2s "**"
{txt}  7{com}.                 {c )-}
{txt}  8{com}.                 else if (r2_`dep'_pv[1,1]>0.05) & (r2_`dep'_pv[1,1]<=0.10) {c -(}
{txt}  9{com}.                         local star_`dep' %1s "*"
{txt} 10{com}.                 {c )-}
{txt} 11{com}.                 else {c -(}
{txt} 12{com}.                         local star_`dep'  ""
{txt} 13{com}.                 {c )-}
{txt} 14{com}. {c )-} 
{txt}
{com}. 
. set seed 1
{txt}
{com}. rwolf DT_score_pre_std rosen_pre_std cpcs_pre_std hhmember hhheadage hhheadeduyear, indepvar(treatment) reps(1000)
Bootstrap replications (1000). This may take some time.
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Romano-Wolf step-down adjusted p-values


Independent variable:  treatment
Outcome variables:   DT_score_pre_std rosen_pre_std cpcs_pre_std hhmember
{col 22}hhheadage hhheadeduyear
Number of resamples: 1000


{hline 78}
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
{hline 20}+{hline 57}
   {txt}DT_score_pre_std {c |}    {res}0.5518             0.5495              0.8312
      {txt}rosen_pre_std {c |}    {res}0.4689             0.4496              0.8312
       {txt}cpcs_pre_std {c |}    {res}0.0105             0.0160              0.0589
           {txt}hhmember {c |}    {res}0.1345             0.1469              0.4635
          {txt}hhheadage {c |}    {res}0.9229             0.9201              0.9201
      {txt}hhheadeduyear {c |}    {res}0.0494             0.0500              0.2478
{hline 78}
{txt}
{com}. set seed 1
{txt}
{com}. rwolf q2a q2c q2h tutor study_other followup_cog_std RSES_std CPCS_std, indepvar(treatment) reps(1000)
Bootstrap replications (1000). This may take some time.
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Romano-Wolf step-down adjusted p-values


Independent variable:  treatment
Outcome variables:   q2a q2c q2h tutor study_other followup_cog_std RSES_std CPCS_std
Number of resamples: 1000


{hline 78}
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
{hline 20}+{hline 57}
                {txt}q2a {c |}    {res}0.7471             0.7493              0.7872
                {txt}q2c {c |}    {res}0.4722             0.4595              0.7872
                {txt}q2h {c |}    {res}0.2801             0.2717              0.5764
              {txt}tutor {c |}    {res}0.0573             0.0460              0.2298
        {txt}study_other {c |}    {res}0.6083             0.6234              0.7872
   {txt}followup_cog_std {c |}    {res}0.0809             0.0799              0.2697
           {txt}RSES_std {c |}    {res}0.0030             0.0030              0.0110
           {txt}CPCS_std {c |}    {res}0.0011             0.0020              0.0060
{hline 78}
{txt}
{com}. 
. 
. /// Table
> tempname hh2
{txt}
{com}. file open `hh2' using "$path_output/summary_stat.tex", write replace
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "% Author: Kazuma Takakura" _newline
{txt}
{com}. file write `hh2' "% Date: `c(current_date)'" _newline
{txt}
{com}. file write `hh2' "% Time: `c(current_time)'" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. 
. file write `hh2' "\begin{c -(}table{c )-}[h!]\footnotesize" _newline
{txt}
{com}. file write `hh2' "  \centering" _newline
{txt}
{com}. file write `hh2' "  \caption{c -(}Summary Statistics{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:sumstat{c )-}" _newline
{txt}
{com}. file write `hh2' "\scalebox{c -(}1{c )-}{c -(}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}threeparttable{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "\begin{c -(}tabular{c )-}{c -(}lccccc{c )-}\toprule" _newline
{txt}
{com}. 
.   
. file write `hh2' " Dependent Variable & Treatment &  Control  & Difference & N   \\\midrule\midrule" _newline
{txt}
{com}. file write `hh2' " Panel A: Baseline & & & &   \\ " _newline
{txt}
{com}. file write `hh2' " DT score^{c -(}a{c )-} & " %04.3f (mean_tr_bl[1,1]) " & " %04.3f (mean_ct_bl[1,1]) " & " %04.3f (r2_DT_score_pre_std_mean[1,1]) `star_DT_score_pre_std' " & " (n_bl[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_bl[1,1]) " ] & [ " %04.3f (sd_ct_bl[1,1]) " ] & ( " %04.3f (r2_DT_score_pre_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.831) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. 
. file write `hh2' " RSES score^{c -(}a{c )-} & " %04.3f (mean_tr_bl[1,2]) " & " %04.3f (mean_ct_bl[1,2]) " & " %04.3f (r2_rosen_pre_std_mean[1,1]) `star_rosen_pre_std' " & "  (n_bl[1,2]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_bl[1,2]) " ] & [ " %04.3f (sd_ct_bl[1,2]) " ] & ( " %04.3f (r2_rosen_pre_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.831) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " CPCS score^{c -(}a{c )-} & " %04.3f (mean_tr_bl[1,3]) " & " %04.3f (mean_ct_bl[1,3]) " & " %04.3f (r2_cpcs_pre_std_mean[1,1]) `star_cpcs_pre_std' " & "  (n_bl[1,3]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_bl[1,3]) " ] & [ " %04.3f (sd_ct_bl[1,3]) " ] & ( " %04.3f (r2_cpcs_pre_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.059) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Household size & " %04.3f (mean_tr_parent[1,1]) " & " %04.3f (mean_ct_parent[1,1]) " & " %04.3f (r2_hhmember_mean[1,1]) `star_hhmember'  " & " (n_parent[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_parent[1,1]) " ] & [ " %04.3f (sd_ct_parent[1,1]) " ] & ( " %04.3f (r2_hhmember_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.464) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Household head age & " %04.3f (mean_tr_parent[1,2]) " & " %04.3f (mean_ct_parent[1,2]) " & " %04.3f (r2_hhheadage_mean[1,1]) `star_hhheadage' " & "  (n_parent[1,2]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_parent[1,2]) " ] & [ " %04.3f (sd_ct_parent[1,2]) " ] & ( " %04.3f (r2_hhheadage_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.920) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Household head education & " %04.3f (mean_tr_parent[1,3]) " & " %04.3f (mean_ct_parent[1,3]) " & " %04.3f (r2_hhheadeduyear_mean[1,1]) `star_hhheadeduyear' " & "  (n_parent[1,3]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_parent[1,3]) " ] & [ " %04.3f (sd_ct_parent[1,3]) " ] & ( " %04.3f (r2_hhheadeduyear_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.248) " \{c )-} &   \\ " _newline
{txt}
{com}. file write `hh2' " \\ "_newline
{txt}
{com}. 
. file write `hh2' " Panel B: Follow-up & & & &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " School attendance & " %04.3f (mean_tr_school[1,1]) " & " %04.3f (mean_ct_school[1,1]) " & " %04.3f (r2_q2a_mean[1,1]) `star_q2a' " & " (n_school[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_school[1,1]) " ] & [ " %04.3f (sd_ct_school[1,1]) " ] & ( " %04.3f (r2_q2a_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.787) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Grade repeat & " %04.3f (mean_tr_school[1,3]) " & " %04.3f (mean_ct_school[1,3]) " & " %04.3f (r2_q2c_mean[1,1]) `star_q2c' " & "  (n_school[1,3]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_school[1,3]) " ] & [ " %04.3f (sd_ct_school[1,3]) " ] & ( " %04.3f (r2_q2c_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.787) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Drop out & " %04.3f (mean_tr_school[1,4]) " & " %04.3f (mean_ct_school[1,4]) " & " %04.3f (r2_q2h_mean[1,1]) `star_q2h'  " & "  (n_school[1,4]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_school[1,4]) " ] & [ " %04.3f (sd_ct_school[1,4]) " ] & ( " %04.3f (r2_q2h_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.576) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Tutoring & " %04.3f (mean_tr_study[1,1]) " & " %04.3f (mean_ct_study[1,1]) " & " %04.3f (r2_tutor_mean[1,1]) `star_tutor'  " & " (n_study[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_study[1,1]) " ] & [ " %04.3f (sd_ct_study[1,1]) " ] & ( " %04.3f (r2_tutor_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.230) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Self-study & " %04.3f (mean_tr_study[1,2]) " & " %04.3f (mean_ct_study[1,2]) " & " %04.3f (r2_study_other_mean[1,1]) `star_study_other' " & "  (n_study[1,2]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_study[1,2]) " ] & [ " %04.3f (sd_ct_study[1,2]) " ] & ( " %04.3f (r2_study_other_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.787) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Rapid math test score^{c -(}a{c )-} & " %04.3f (mean_tr_cog[1,1]) " & " %04.3f (mean_ct_cog[1,1]) " & " %04.3f (r2_followup_cog_std_mean[1,1]) `star_followup_cog_std'  "  & "  (n_cog[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_cog[1,1]) " ] & [ " %04.3f (sd_ct_cog[1,1]) " ] & ( " %04.3f (r2_followup_cog_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.270) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " RSES score^{c -(}a{c )-} & " %04.3f (mean_tr_noncog[1,2]) " & " %04.3f (mean_ct_noncog[1,2]) " & " %04.3f (r2_RSES_std_mean[1,1])   `star_RSES_std' " & " (n_noncog[1,2]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_noncog[1,2]) " ] & [ " %04.3f (sd_ct_noncog[1,2]) " ] & ( " %04.3f (r2_RSES_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.011) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " CPCS score^{c -(}a{c )-} & " %04.3f (mean_tr_noncog[1,3]) " & " %04.3f (mean_ct_noncog[1,3]) " & " %04.3f (r2_CPCS_std_mean[1,1])   `star_CPCS_std' "&  " (n_noncog[1,3]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_noncog[1,3]) " ] & [ " %04.3f (sd_ct_noncog[1,3]) " ] & ( " %04.3f (r2_CPCS_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.006) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. 
. file write `hh2' "\midrule" _newline
{txt}
{com}. file write `hh2' "\end{c -(}tabular{c )-}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\item (a) Variables are standardized using the average and variance of the whole follow-up sample in the February 2022 survey. " _newline
{txt}
{com}. file write `hh2' "\item (b) Standard deviations are reported in square brackets.  " _newline
{txt}
{com}. file write `hh2' "\item (c) Wild clustered bootstrap p-values are reported within parentheses. Clusters are schools at the baseline. There are 34 clusters. " _newline
{txt}
{com}. file write `hh2' "\item (d) Romano-Wolf multiple hypothesis testing p-values are reported in curly brackets. This test is conducted separately for the baseline variables and the follow-up variables." _newline
{txt}
{com}. file write `hh2' "\item (e) Statistical significance is indicated by stars based on the wild clustered bootstrap p-values reported in parentheses: $*$ denotes significance at the 10\% level, $∗∗$ at the 5\% level, and $∗∗∗$ at the 1\% level.  " _newline
{txt}
{com}. file write `hh2' "\end{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}threeparttable{c )-}" _newline
{txt}
{com}. file write `hh2' "{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}table{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. file close `hh2'
{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/mb/s9qljd594gbfyhl2dqnnwlcw0000gn/T//SD56186.000000"
{txt}
{com}. * This is the do file to create "Table 1. Summary Statistics"
. set seed 123
{txt}
{com}. 
. use "$path_data/temp/followup_student_parents_matched", clear
{txt}
{com}. 
. corr rosen_pre_std cpcs_pre_std
{txt}(obs=243)

             {c |} rosen_~d cpcs_p~d
{hline 13}{c +}{hline 18}
rosen_pre_~d {c |}{res}   1.0000
{txt}cpcs_pre_std {c |}{res}   0.9026   1.0000

{txt}
{com}. corr RSES_std CPCS_std
{txt}(obs=236)

             {c |} RSES_std CPCS_std
{hline 13}{c +}{hline 18}
    RSES_std {c |}{res}   1.0000
    {txt}CPCS_std {c |}{res}   0.9701   1.0000

{txt}
{com}. 
. 
. /// Varable Selection
> /// Baseline
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat DT_score_pre_std rosen_pre_std cpcs_pre_std if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_bl = r(StatTotal)
{txt}  5{com}. 
. tabstat DT_score_pre_std rosen_pre_std cpcs_pre_std if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_bl = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}      144       145       145
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   DT_score_p~d  rosen_pre_~d  cpcs_pre_std
N {res}          144           145           145
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}       95        98        98
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   DT_score_p~d  rosen_pre_~d  cpcs_pre_std
N {res}           95            98            98
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res}-.0313509  .0382918  .1345164
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      DT_score_p~d  rosen_pre_~d  cpcs_pre_std
Mean {res}   -.03135095     .03829184      .1345164
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .0475214 -.0566567 -.1990291
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      DT_score_p~d  rosen_pre_~d  cpcs_pre_std
Mean {res}    .04752144    -.05665667    -.19902912
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} 1.023177  .9748496  .9271749
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    DT_score_p~d  rosen_pre_~d  cpcs_pre_std
SD {res}    1.0231772     .97484957     .92717486
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} .9672202  1.038561  1.073121
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    DT_score_p~d  rosen_pre_~d  cpcs_pre_std
SD {res}    .96722024      1.038561     1.0731214
{reset}
{com}. 
. matrix n_bl = J(1,3,.)
{txt}
{com}. forvalues i = 1/3 {c -(}
{txt}  2{com}.         matrix n_bl[1,`i'] = n_tr_bl[1,`i'] + n_ct_bl[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in DT_score_pre_std rosen_pre_std cpcs_pre_std{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}239
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 32
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  2
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.5
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        DT_score_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0788724{col 38}{space 1}  -0.38{col 46}{space 3}0.726{col 54}{space 3}-.5170202{col 66}{space 3} .3849625
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}           rosen_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0949485{col 38}{space 1}   0.47{col 46}{space 3}0.600{col 54}{space 3}-.3528281{col 66}{space 3} .5243389
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}            cpcs_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3335455{col 38}{space 1}   1.82{col 46}{space 3}0.116{col 54}{space 3}-.0763347{col 66}{space 3} .7082129
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. /// Family
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat hhmember hhheadage hhheadeduyear if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_parent = r(StatTotal)
{txt}  5{com}. 
. tabstat hhmember hhheadage hhheadeduyear if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_parent = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}      145       145       145
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
       hhmember     hhheadage  hhheadeduy~r
N {res}          145           145           145
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}       98        98        98
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
       hhmember     hhheadage  hhheadeduy~r
N {res}           98            98            98
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res} 4.510345  46.57241  2.331034
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
          hhmember     hhheadage  hhheadeduy~r
Mean {res}    4.5103448     46.572414     2.3310345
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res} 4.265306  46.68878  3.163265
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
          hhmember     hhheadage  hhheadeduy~r
Mean {res}    4.2653061     46.688776     3.1632653
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} 1.280827   9.03907  2.995495
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
        hhmember     hhheadage  hhheadeduy~r
SD {res}    1.2808268     9.0390702     2.9954947
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} 1.197515  9.408681  3.530993
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
        hhmember     hhheadage  hhheadeduy~r
SD {res}    1.1975148     9.4086808     3.5309935
{reset}
{com}. 
. matrix n_parent = J(1,3,.)
{txt}
{com}. forvalues i = 1/3 {c -(}
{txt}  2{com}.         matrix n_parent[1,`i'] = n_tr_parent[1,`i'] + n_ct_parent[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in hhmember hhheadage hhheadeduyear{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                hhmember{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2450387{col 38}{space 1}   1.29{col 46}{space 3}0.190{col 54}{space 3}-.1378142{col 66}{space 3} .7032024
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}               hhheadage{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.1163617{col 38}{space 1}  -0.07{col 46}{space 3}0.914{col 54}{space 3}-3.252753{col 66}{space 3} 3.440111
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}           hhheadeduyear{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.8322308{col 38}{space 1}  -2.22{col 46}{space 3}0.018{col 54}{space 3}-1.589777{col 66}{space 3}-.0728916
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. 
. 
. /// School　attendance
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat q2a q2b q2c q2h if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_school = r(StatTotal)
{txt}  5{com}. 
. tabstat q2a q2b q2c q2h if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_school = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:N} {...}
{c |}{...}
 {res}      145       145       145       145
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
   q2a  q2b  q2c  q2h
N {res} 145  145  145  145
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:N} {...}
{c |}{...}
 {res}       98        98        98        98
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
   q2a  q2b  q2c  q2h
N {res}  98   98   98   98
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .5517241  9.606897   .062069  .3793103
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
            q2a        q2b        q2c        q2h
Mean {res} .55172414  9.6068966  .06206897  .37931034
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .5306122  9.602041  .0408163  .4489796
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
            q2a        q2b        q2c        q2h
Mean {res} .53061224  9.6020408  .04081633  .44897959
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:SD} {...}
{c |}{...}
 {res} .4990412  1.029405  .2421171  .4868973
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
          q2a        q2b        q2c        q2h
SD {res} .49904123  1.0294048   .2421171  .48689728
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:SD} {...}
{c |}{...}
 {res} .5016279  .8703571  .1988818  .4999474
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
          q2a        q2b        q2c        q2h
SD {res}  .5016279  .87035715  .19888179   .4999474
{reset}
{com}. 
. matrix n_school = J(1,4,.)
{txt}
{com}. forvalues i = 1/4 {c -(}
{txt}  2{com}.         matrix n_school[1,`i'] = n_tr_school[1,`i'] + n_ct_school[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in q2a q2b q2c q2h{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                     q2a{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0211119{col 38}{space 1}   0.25{col 46}{space 3}0.868{col 54}{space 3}-.1592541{col 66}{space 3} .2083223
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                     q2b{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0048557{col 38}{space 1}   0.03{col 46}{space 3}0.990{col 54}{space 3}-.3899922{col 66}{space 3}  .336345
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                     q2c{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0212526{col 38}{space 1}   0.56{col 46}{space 3}0.662{col 54}{space 3}-.0533022{col 66}{space 3} .0982309
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                     q2h{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0696692{col 38}{space 1}  -0.85{col 46}{space 3}0.446{col 54}{space 3}-.2494278{col 66}{space 3} .1087737
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. /// Other study variable
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat tutor study_other study_affect_covid hometutoring onlineclass studymyself parentsteach if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_study = r(StatTotal)
{txt}  5{com}. 
. tabstat tutor study_other study_affect_covid hometutoring onlineclass studymyself parentsteach if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_study = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:N} {...}
{c |}{...}
 {res}      145       145       145       145       145       145       145
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
          tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
N {res}          145           145           145           145           145           145           145
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:N} {...}
{c |}{...}
 {res}       98        98        98        98        98        98        98
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
          tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
N {res}           98            98            98            98            98            98            98
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:Mean} {...}
{c |}{...}
 {res}  .337931   .462069  .6482759  .0965517  .0482759  .5241379  .0275862
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
             tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
Mean {res}    .33793103     .46206897     .64827586     .09655172     .04827586     .52413793     .02758621
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .4591837  .4285714  .6020408  .1428571  .1326531  .5306122  .1938776
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
             tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
Mean {res}    .45918367     .42857143     .60204082     .14285714     .13265306     .53061224     .19387755
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:SD} {...}
{c |}{...}
 {res} .4746445  .5002873  .4791635  .2963701  .2150915  .5011481  .1643517
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
           tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
SD {res}    .47464445     .50028727     .47916354     .29637012     .21509153     .50114811     .16435174
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:SD} {...}
{c |}{...}
 {res} .5008934   .497416  .4919935  .3517262  .3409434  .5016279  .3973667
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
           tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
SD {res}    .50089337       .497416     .49199354     .35172623     .34094336      .5016279     .39736667
{reset}
{com}. 
. matrix n_study = J(1,8,.)
{txt}
{com}. forvalues i = 1/8 {c -(}
{txt}  2{com}.         matrix n_study[1,`i'] = n_tr_study[1,`i'] + n_ct_study[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in tutor study_other study_affect_covid hometutoring onlineclass studymyself parentsteach{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                   tutor{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.1212526{col 38}{space 1}  -1.69{col 46}{space 3}0.120{col 54}{space 3}-.2773205{col 66}{space 3} .0421174
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}             study_other{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0334975{col 38}{space 1}   0.39{col 46}{space 3}0.742{col 54}{space 3}-.1488081{col 66}{space 3} .2186319
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}      study_affect_covid{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  .046235{col 38}{space 1}   0.56{col 46}{space 3}0.576{col 54}{space 3} -.124707{col 66}{space 3} .2364771
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}            hometutoring{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0463054{col 38}{space 1}  -1.11{col 46}{space 3}0.312{col 54}{space 3} -.128302{col 66}{space 3} .0430425
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}             onlineclass{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0843772{col 38}{space 1}  -1.92{col 46}{space 3}0.096{col 54}{space 3} -.179885{col 66}{space 3} .0148069
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}             studymyself{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0064743{col 38}{space 1}  -0.08{col 46}{space 3}0.940{col 54}{space 3}-.1754887{col 66}{space 3} .1699468
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}            parentsteach{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.1662913{col 38}{space 1}  -3.85{col 46}{space 3}0.000{col 54}{space 3} -.255542{col 66}{space 3}-.0629773
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. /// Cognitive
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat followup_cog_std if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_cog = r(StatTotal)
{txt}  5{com}. 
. tabstat followup_cog_std if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_cog = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}{ralign 12:Variable} {...}
{c |}         N
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res}      145
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
   followup_c~d
N {res}          145
{reset}
{ralign 12:Variable} {...}
{c |}         N
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res}       98
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
   followup_c~d
N {res}           98
{reset}
{ralign 12:Variable} {...}
{c |}      Mean
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res}-.0920409
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
      followup_c~d
Mean {res}   -.09204085
{reset}
{ralign 12:Variable} {...}
{c |}      Mean
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res} .1361831
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
      followup_c~d
Mean {res}    .13618309
{reset}
{ralign 12:Variable} {...}
{c |}        SD
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res} 1.070796
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
    followup_c~d
SD {res}     1.070796
{reset}
{ralign 12:Variable} {...}
{c |}        SD
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res} .8725076
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
    followup_c~d
SD {res}    .87250763
{reset}
{com}. 
. matrix n_cog = J(1,1,.)
{txt}
{com}. forvalues i = 1/1 {c -(}
{txt}  2{com}.         matrix n_cog[1,`i'] = n_tr_cog[1,`i'] + n_ct_cog[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2282239{col 38}{space 1}  -1.36{col 46}{space 3}0.192{col 54}{space 3}-.5930653{col 66}{space 3} .1293723
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}.         
. matrix r2_followup_cog_std_temp = r(table)
{txt}
{com}. 
. 
. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix r2_followup_cog_std_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix r2_followup_cog_std_mean[1,`j'] = r2_followup_cog_std_temp[1,`j']
{txt}  3{com}. * standard error
. * matrix r2_followup_cog_std_se[1,`j'] = r2_followup_cog_std_temp[2,`j']
. * p value
. matrix r2_followup_cog_std_pv[1,`j'] = r2_followup_cog_std_temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}.     
. /// Non cognitive
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat followup_noncog_std RSES_std CPCS_std if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_noncog = r(StatTotal)
{txt}  5{com}. 
. tabstat followup_noncog_std RSES_std CPCS_std if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_noncog = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}      105       140       140
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   followup_n~d      RSES_std      CPCS_std
N {res}          105           140           140
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}       74        96        96
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   followup_n~d      RSES_std      CPCS_std
N {res}           74            96            96
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .1969319  .1591241  .1745941
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      followup_n~d      RSES_std      CPCS_std
Mean {res}    .19693189      .1591241     .17459415
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res}-.2794302 -.2320565  -.254617
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      followup_n~d      RSES_std      CPCS_std
Mean {res}   -.27943024    -.23205648    -.25461705
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} 1.006158  1.022691  1.008304
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    followup_n~d      RSES_std      CPCS_std
SD {res}    1.0061577     1.0226907     1.0083041
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} .9279901  .9228443  .9357831
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    followup_n~d      RSES_std      CPCS_std
SD {res}    .92799012     .92284427     .93578307
{reset}
{com}. 
. matrix n_noncog = J(1,3,.)
{txt}
{com}. forvalues i = 1/3 {c -(}
{txt}  2{com}.         matrix n_noncog[1,`i'] = n_tr_noncog[1,`i'] + n_ct_noncog[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in followup_noncog_std RSES_std CPCS_std{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}179
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 32
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}5.6
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}     followup_noncog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .4763621{col 38}{space 1}   2.08{col 46}{space 3}0.074{col 54}{space 3}-.0489589{col 66}{space 3} .9729653
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}236
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.2
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3911806{col 38}{space 1}   2.02{col 46}{space 3}0.064{col 54}{space 3}-.0321475{col 66}{space 3} .7806997
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}236
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.2
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .4292112{col 38}{space 1}   2.26{col 46}{space 3}0.038{col 54}{space 3} .0238533{col 66}{space 3} .7985476
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. 
. /// Behavioral
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat hyper if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_hyper = r(StatTotal)
{txt}  5{com}. 
. tabstat hyper if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_hyper = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}{ralign 12:Variable} {...}
{c |}         N
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res}      113
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
   hyper
N {res}   113
{reset}
{ralign 12:Variable} {...}
{c |}         N
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res}       71
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
   hyper
N {res}    71
{reset}
{ralign 12:Variable} {...}
{c |}      Mean
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res} .2654867
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
          hyper
Mean {res} .26548673
{reset}
{ralign 12:Variable} {...}
{c |}      Mean
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res} .0704225
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
          hyper
Mean {res} .07042254
{reset}
{ralign 12:Variable} {...}
{c |}        SD
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res}  .443559
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
        hyper
SD {res} .44355905
{reset}
{ralign 12:Variable} {...}
{c |}        SD
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res} .2576789
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
        hyper
SD {res} .25767885
{reset}
{com}. 
. matrix n_hyper = J(1,1,.)
{txt}
{com}. forvalues i = 1/1 {c -(}
{txt}  2{com}.         matrix n_hyper[1,`i'] = n_tr_hyper[1,`i'] + n_ct_hyper[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in hyper{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment if hypernoinfo == 0, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}184
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}5.6
{col 69}{txt}max{col 72} = {res} 12
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                   hyper{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1950642{col 38}{space 1}   3.37{col 46}{space 3}0.010{col 54}{space 3} .0645109{col 66}{space 3} .3241339
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. 
. // significant level
. 
. local outcome DT_score_pre_std rosen_pre_std cpcs_pre_std hhmember hhheadage hhheadeduyear q2a q2c q2h tutor study_other followup_cog_std RSES_std CPCS_std
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}.                 if r2_`dep'_pv[1,1]<=0.01 {c -(}
{txt}  3{com}.                         local star_`dep' %3s "***"
{txt}  4{com}.                 {c )-}
{txt}  5{com}.                 else if (r2_`dep'_pv[1,1]>0.01) & (r2_`dep'_pv[1,1]<=0.05) {c -(}
{txt}  6{com}.                         local star_`dep' %2s "**"
{txt}  7{com}.                 {c )-}
{txt}  8{com}.                 else if (r2_`dep'_pv[1,1]>0.05) & (r2_`dep'_pv[1,1]<=0.10) {c -(}
{txt}  9{com}.                         local star_`dep' %1s "*"
{txt} 10{com}.                 {c )-}
{txt} 11{com}.                 else {c -(}
{txt} 12{com}.                         local star_`dep'  ""
{txt} 13{com}.                 {c )-}
{txt} 14{com}. {c )-} 
{txt}
{com}. 
. set seed 123
{txt}
{com}. rwolf DT_score_pre_std rosen_pre_std cpcs_pre_std hhmember hhheadage hhheadeduyear, indepvar(treatment) reps(1000)
Bootstrap replications (1000). This may take some time.
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Romano-Wolf step-down adjusted p-values


Independent variable:  treatment
Outcome variables:   DT_score_pre_std rosen_pre_std cpcs_pre_std hhmember
{col 22}hhheadage hhheadeduyear
Number of resamples: 1000


{hline 78}
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
{hline 20}+{hline 57}
   {txt}DT_score_pre_std {c |}    {res}0.5518             0.5475              0.8541
      {txt}rosen_pre_std {c |}    {res}0.4689             0.4925              0.8541
       {txt}cpcs_pre_std {c |}    {res}0.0105             0.0140              0.0539
           {txt}hhmember {c |}    {res}0.1345             0.1209              0.4366
          {txt}hhheadage {c |}    {res}0.9229             0.9161              0.9161
      {txt}hhheadeduyear {c |}    {res}0.0494             0.0480              0.2168
{hline 78}
{txt}
{com}. set seed 123
{txt}
{com}. rwolf q2a q2c q2h tutor study_other followup_cog_std RSES_std CPCS_std, indepvar(treatment) reps(1000)
Bootstrap replications (1000). This may take some time.
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Romano-Wolf step-down adjusted p-values


Independent variable:  treatment
Outcome variables:   q2a q2c q2h tutor study_other followup_cog_std RSES_std CPCS_std
Number of resamples: 1000


{hline 78}
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
{hline 20}+{hline 57}
                {txt}q2a {c |}    {res}0.7471             0.7662              0.8022
                {txt}q2c {c |}    {res}0.4722             0.4595              0.8022
                {txt}q2h {c |}    {res}0.2801             0.3057              0.6324
              {txt}tutor {c |}    {res}0.0573             0.0539              0.2498
        {txt}study_other {c |}    {res}0.6083             0.6294              0.8022
   {txt}followup_cog_std {c |}    {res}0.0809             0.0689              0.2787
           {txt}RSES_std {c |}    {res}0.0030             0.0050              0.0200
           {txt}CPCS_std {c |}    {res}0.0011             0.0030              0.0110
{hline 78}
{txt}
{com}. 
. 
. /// Table
> tempname hh2
{txt}
{com}. file open `hh2' using "$path_output/summary_stat.tex", write replace
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "% Author: Kazuma Takakura" _newline
{txt}
{com}. file write `hh2' "% Date: `c(current_date)'" _newline
{txt}
{com}. file write `hh2' "% Time: `c(current_time)'" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. 
. file write `hh2' "\begin{c -(}table{c )-}[h!]\footnotesize" _newline
{txt}
{com}. file write `hh2' "  \centering" _newline
{txt}
{com}. file write `hh2' "  \caption{c -(}Summary Statistics{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:sumstat{c )-}" _newline
{txt}
{com}. file write `hh2' "\scalebox{c -(}1{c )-}{c -(}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}threeparttable{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "\begin{c -(}tabular{c )-}{c -(}lccccc{c )-}\toprule" _newline
{txt}
{com}. 
.   
. file write `hh2' " Dependent Variable & Treatment &  Control  & Difference & N   \\\midrule\midrule" _newline
{txt}
{com}. file write `hh2' " Panel A: Baseline & & & &   \\ " _newline
{txt}
{com}. file write `hh2' " DT score^{c -(}a{c )-} & " %04.3f (mean_tr_bl[1,1]) " & " %04.3f (mean_ct_bl[1,1]) " & " %04.3f (r2_DT_score_pre_std_mean[1,1]) `star_DT_score_pre_std' " & " (n_bl[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_bl[1,1]) " ] & [ " %04.3f (sd_ct_bl[1,1]) " ] & ( " %04.3f (r2_DT_score_pre_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.831) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. 
. file write `hh2' " RSES score^{c -(}a{c )-} & " %04.3f (mean_tr_bl[1,2]) " & " %04.3f (mean_ct_bl[1,2]) " & " %04.3f (r2_rosen_pre_std_mean[1,1]) `star_rosen_pre_std' " & "  (n_bl[1,2]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_bl[1,2]) " ] & [ " %04.3f (sd_ct_bl[1,2]) " ] & ( " %04.3f (r2_rosen_pre_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.831) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " CPCS score^{c -(}a{c )-} & " %04.3f (mean_tr_bl[1,3]) " & " %04.3f (mean_ct_bl[1,3]) " & " %04.3f (r2_cpcs_pre_std_mean[1,1]) `star_cpcs_pre_std' " & "  (n_bl[1,3]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_bl[1,3]) " ] & [ " %04.3f (sd_ct_bl[1,3]) " ] & ( " %04.3f (r2_cpcs_pre_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.059) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Household size & " %04.3f (mean_tr_parent[1,1]) " & " %04.3f (mean_ct_parent[1,1]) " & " %04.3f (r2_hhmember_mean[1,1]) `star_hhmember'  " & " (n_parent[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_parent[1,1]) " ] & [ " %04.3f (sd_ct_parent[1,1]) " ] & ( " %04.3f (r2_hhmember_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.464) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Household head age & " %04.3f (mean_tr_parent[1,2]) " & " %04.3f (mean_ct_parent[1,2]) " & " %04.3f (r2_hhheadage_mean[1,1]) `star_hhheadage' " & "  (n_parent[1,2]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_parent[1,2]) " ] & [ " %04.3f (sd_ct_parent[1,2]) " ] & ( " %04.3f (r2_hhheadage_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.920) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Household head education & " %04.3f (mean_tr_parent[1,3]) " & " %04.3f (mean_ct_parent[1,3]) " & " %04.3f (r2_hhheadeduyear_mean[1,1]) `star_hhheadeduyear' " & "  (n_parent[1,3]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_parent[1,3]) " ] & [ " %04.3f (sd_ct_parent[1,3]) " ] & ( " %04.3f (r2_hhheadeduyear_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.248) " \{c )-} &   \\ " _newline
{txt}
{com}. file write `hh2' " \\ "_newline
{txt}
{com}. 
. file write `hh2' " Panel B: Follow-up & & & &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " School attendance & " %04.3f (mean_tr_school[1,1]) " & " %04.3f (mean_ct_school[1,1]) " & " %04.3f (r2_q2a_mean[1,1]) `star_q2a' " & " (n_school[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_school[1,1]) " ] & [ " %04.3f (sd_ct_school[1,1]) " ] & ( " %04.3f (r2_q2a_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.787) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Grade repeat & " %04.3f (mean_tr_school[1,3]) " & " %04.3f (mean_ct_school[1,3]) " & " %04.3f (r2_q2c_mean[1,1]) `star_q2c' " & "  (n_school[1,3]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_school[1,3]) " ] & [ " %04.3f (sd_ct_school[1,3]) " ] & ( " %04.3f (r2_q2c_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.787) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Drop out & " %04.3f (mean_tr_school[1,4]) " & " %04.3f (mean_ct_school[1,4]) " & " %04.3f (r2_q2h_mean[1,1]) `star_q2h'  " & "  (n_school[1,4]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_school[1,4]) " ] & [ " %04.3f (sd_ct_school[1,4]) " ] & ( " %04.3f (r2_q2h_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.576) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Tutoring & " %04.3f (mean_tr_study[1,1]) " & " %04.3f (mean_ct_study[1,1]) " & " %04.3f (r2_tutor_mean[1,1]) `star_tutor'  " & " (n_study[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_study[1,1]) " ] & [ " %04.3f (sd_ct_study[1,1]) " ] & ( " %04.3f (r2_tutor_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.230) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Self-study & " %04.3f (mean_tr_study[1,2]) " & " %04.3f (mean_ct_study[1,2]) " & " %04.3f (r2_study_other_mean[1,1]) `star_study_other' " & "  (n_study[1,2]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_study[1,2]) " ] & [ " %04.3f (sd_ct_study[1,2]) " ] & ( " %04.3f (r2_study_other_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.787) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Rapid math test score^{c -(}a{c )-} & " %04.3f (mean_tr_cog[1,1]) " & " %04.3f (mean_ct_cog[1,1]) " & " %04.3f (r2_followup_cog_std_mean[1,1]) `star_followup_cog_std'  "  & "  (n_cog[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_cog[1,1]) " ] & [ " %04.3f (sd_ct_cog[1,1]) " ] & ( " %04.3f (r2_followup_cog_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.270) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " RSES score^{c -(}a{c )-} & " %04.3f (mean_tr_noncog[1,2]) " & " %04.3f (mean_ct_noncog[1,2]) " & " %04.3f (r2_RSES_std_mean[1,1])   `star_RSES_std' " & " (n_noncog[1,2]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_noncog[1,2]) " ] & [ " %04.3f (sd_ct_noncog[1,2]) " ] & ( " %04.3f (r2_RSES_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.011) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " CPCS score^{c -(}a{c )-} & " %04.3f (mean_tr_noncog[1,3]) " & " %04.3f (mean_ct_noncog[1,3]) " & " %04.3f (r2_CPCS_std_mean[1,1])   `star_CPCS_std' "&  " (n_noncog[1,3]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_noncog[1,3]) " ] & [ " %04.3f (sd_ct_noncog[1,3]) " ] & ( " %04.3f (r2_CPCS_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.006) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. 
. file write `hh2' "\midrule" _newline
{txt}
{com}. file write `hh2' "\end{c -(}tabular{c )-}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\item (a) Variables are standardized using the average and variance of the whole follow-up sample in the February 2022 survey. " _newline
{txt}
{com}. file write `hh2' "\item (b) Standard deviations are reported in square brackets.  " _newline
{txt}
{com}. file write `hh2' "\item (c) Wild clustered bootstrap p-values are reported within parentheses. Clusters are schools at the baseline. There are 34 clusters. " _newline
{txt}
{com}. file write `hh2' "\item (d) Romano-Wolf multiple hypothesis testing p-values are reported in curly brackets. This test is conducted separately for the baseline variables and the follow-up variables." _newline
{txt}
{com}. file write `hh2' "\item (e) Statistical significance is indicated by stars based on the wild clustered bootstrap p-values reported in parentheses: $*$ denotes significance at the 10\% level, $∗∗$ at the 5\% level, and $∗∗∗$ at the 1\% level.  " _newline
{txt}
{com}. file write `hh2' "\end{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}threeparttable{c )-}" _newline
{txt}
{com}. file write `hh2' "{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}table{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. file close `hh2'
{txt}
{com}. 
{txt}end of do-file

{com}. do "/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/do_files/0_allのコピー.do"
{txt}
{com}. version 18.5
{txt}
{com}. clear all
{res}{txt}
{com}. set more off
{txt}
{com}. 
. 
. * set the path to global
. 
. global path_replication "/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package"
{txt}
{com}. 
. global path_output "$path_replication/outputs"
{txt}
{com}. 
. global path_data "$path_replication/data"
{txt}
{com}. 
. global path_do "$path_replication/do_files"
{txt}
{com}. 
. adopath + "$path_replication/ado"
{txt}  [1]  (BASE)      "{res}/Applications/Stata/ado/base/{txt}"
  [2]  (SITE)      "{res}/Applications/Stata/ado/site/{txt}"
  [3]              "{res}.{txt}"
  [4]  (PERSONAL)  "{res}/Users/takakurakazuma/Documents/Stata/ado/personal/{txt}"
  [5]  (PLUS)      "{res}/Users/takakurakazuma/Library/Application Support/Stata/ado/plus/{txt}"
  [6]  (OLDPLACE)  "{res}~/ado/{txt}"
  [7]              "{res}CHANGE TO YOUR PATH/ado{txt}"
  [8]              "{res}/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/ado{txt}"

{com}. 
. 
. 
. * log using "$path_output/log_all", replace
. 
. set seed 123
{txt}
{com}. 
. **************************************************
. 
. 
. *** run the code for cleaning.
. 
. do "$path_do/1_data_cleaning_students.do"
{txt}
{com}. clear all
{res}{txt}
{com}. set more off
{txt}
{com}. 
. 
. /// prepare baseline teacher information
> use "$path_data/original_teacher.dta", clear
{txt}
{com}. drop if endline == 1
{txt}(1,004 observations deleted)

{com}. keep student_no age_tchr gender_tchr edu_tchr
{txt}
{com}. sort student_no
{txt}
{com}. save "$path_data/temp/teacher", replace
{txt}{p 0 4 2}
file {bf}
/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/data/temp/teacher.dta{rm}
saved
{p_end}

{com}. 
. import excel "$path_data/followup_students_master.xlsx", clear first
{res}{text}(255 vars, 287 obs)

{com}. gen student_no = q1b
{txt}
{com}. 
. /// remove imcomplete interview
> drop if q0a == "319003"
{txt}(1 observation deleted)

{com}. 
. /// Yes==1, No==0, Dont know ==.
> recode q2a q2c q2h q3a q3e q4a q7a q7b q9a1 q9a3 q9b1 (2=0)
{txt}(127 changes made to {bf:q2a})
(268 changes made to {bf:q2c})
(173 changes made to {bf:q2h})
(170 changes made to {bf:q3a})
(41 changes made to {bf:q3e})
(24 changes made to {bf:q4a})
(56 changes made to {bf:q7a})
(11 changes made to {bf:q7b})
(24 changes made to {bf:q9a1})
(267 changes made to {bf:q9a3})
(0 changes made to {bf:q9b1})

{com}. recode q9b1 (3=.)
{txt}(2 changes made to {bf:q9b1})

{com}. 
. 
. 
. 
. // other changes
. 
. gen PSC_grade = q2k2
{txt}(62 missing values generated)

{com}. replace PSC_grade ="0" if q2k2== "Auto"
{txt}(5 real changes made)

{com}. replace PSC_grade ="0" if q2k2== "mone nai"
{txt}(1 real change made)

{com}. replace PSC_grade ="0" if q2k2== ""
{txt}(62 real changes made)

{com}. replace PSC_grade ="3.08" if q2k2=="3.o8"
{txt}(1 real change made)

{com}. destring PSC_grade, replace
{txt}PSC_grade: all characters numeric; {res}replaced {txt}as {res}double
{txt}
{com}. recode PSC_grade(0=.)
{txt}(68 changes made to {bf:PSC_grade})

{com}. 
. gen JSC_grade = q2l2
{txt}(146 missing values generated)

{com}. gen JSC_auto = 0
{txt}
{com}. replace JSC_auto = 1 if q2l2 == "Ato pas"
{txt}(3 real changes made)

{com}. replace JSC_auto = 1 if q2l2 == "Atou pass"
{txt}(1 real change made)

{com}. replace JSC_auto = 1 if q2l2 == "Auto"
{txt}(32 real changes made)

{com}. replace JSC_auto = 1 if q2l2 == "Auto  pass"
{txt}(4 real changes made)

{com}. replace JSC_auto = 1 if q2l2 == "Auto Pass"
{txt}(8 real changes made)

{com}. replace JSC_auto = 1 if q2l2 == "Auto pass"
{txt}(22 real changes made)

{com}. replace JSC_auto = 1 if q2l2 == "Autopash."
{txt}(1 real change made)

{com}. replace JSC_auto = 1 if q2l2 == "Autopass"
{txt}(1 real change made)

{com}. replace JSC_auto = 1 if q2l2 == "auto pass"
{txt}(11 real changes made)

{com}. replace JSC_auto = 1 if q2l2 == "result school thake deyni"
{txt}(1 real change made)

{com}. replace JSC_grade = "0" if JSC_auto == 1
{txt}(84 real changes made)

{com}. replace JSC_grade = "0" if q2l2 == ""
{txt}(146 real changes made)

{com}. destring JSC_grade, replace
{txt}JSC_grade: all characters numeric; {res}replaced {txt}as {res}double
{txt}
{com}. recode JSC_grade(0=.)
{txt}(230 changes made to {bf:JSC_grade})

{com}. 
. 
. local q5an 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
{txt}
{com}. foreach i in `q5an'{c -(}
{txt}  2{com}. gen q5a_`i' = 1 if q5a==`i'
{txt}  3{com}. recode q5a_`i'(.=0)
{txt}  4{com}. {c )-}
{txt}(285 missing values generated)
(285 changes made to {bf:q5a_1})
(270 missing values generated)
(270 changes made to {bf:q5a_2})
(265 missing values generated)
(265 changes made to {bf:q5a_3})
(188 missing values generated)
(188 changes made to {bf:q5a_4})
(264 missing values generated)
(264 changes made to {bf:q5a_5})
(284 missing values generated)
(284 changes made to {bf:q5a_6})
(282 missing values generated)
(282 changes made to {bf:q5a_7})
(263 missing values generated)
(263 changes made to {bf:q5a_8})
(285 missing values generated)
(285 changes made to {bf:q5a_9})
(286 missing values generated)
(286 changes made to {bf:q5a_10})
(286 missing values generated)
(286 changes made to {bf:q5a_11})
(217 missing values generated)
(217 changes made to {bf:q5a_12})
(279 missing values generated)
(279 changes made to {bf:q5a_13})
(286 missing values generated)
(286 changes made to {bf:q5a_14})
(284 missing values generated)
(284 changes made to {bf:q5a_15})
(286 missing values generated)
(286 changes made to {bf:q5a_16})

{com}. 
. local q5bn 1 2 3 4 5 6 7 8 9
{txt}
{com}. foreach i in `q5bn'{c -(}
{txt}  2{com}. gen q5b_`i' = 1 if q5b==`i'
{txt}  3{com}. recode q5b_`i'(.=0)
{txt}  4{com}. {c )-}
{txt}(243 missing values generated)
(243 changes made to {bf:q5b_1})
(189 missing values generated)
(189 changes made to {bf:q5b_2})
(260 missing values generated)
(260 changes made to {bf:q5b_3})
(281 missing values generated)
(281 changes made to {bf:q5b_4})
(231 missing values generated)
(231 changes made to {bf:q5b_5})
(285 missing values generated)
(285 changes made to {bf:q5b_6})
(284 missing values generated)
(284 changes made to {bf:q5b_7})
(277 missing values generated)
(277 changes made to {bf:q5b_8})
(283 missing values generated)
(283 changes made to {bf:q5b_9})

{com}. 
. 
. 
. gen q6a1_correct = 1 if q6a1==10800
{txt}(88 missing values generated)

{com}. gen q6a2_correct = 1 if q6a2==9
{txt}(43 missing values generated)

{com}. gen q6a3a_correct = 1 if q6a3a==70
{txt}(70 missing values generated)

{com}. gen q6a3b_correct = 1 if q6a3b==50
{txt}(46 missing values generated)

{com}. gen q6a4_correct = 1 if q6a4==20
{txt}(211 missing values generated)

{com}. gen q6a5_correct = 1 if q6a5==5
{txt}(224 missing values generated)

{com}. 
. recode q6a1_correct q6a2_correct q6a3a_correct q6a3b_correct q6a4_correct q6a5_correct (.=0)
{txt}(88 changes made to {bf:q6a1_correct})
(43 changes made to {bf:q6a2_correct})
(70 changes made to {bf:q6a3a_correct})
(46 changes made to {bf:q6a3b_correct})
(211 changes made to {bf:q6a4_correct})
(224 changes made to {bf:q6a5_correct})

{com}. 
. save "$path_data/temp/followup_student_data", replace
{txt}{p 0 4 2}
file {bf}
/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/data/temp/followup_student_data.dta{rm}
saved
{p_end}

{com}.  
. 
. import excel "$path_data/followup_students_extra.xlsx",  clear first
{res}{text}(50 vars, 222 obs)

{com}. drop if q1b==1223 & _index==64
{txt}(1 observation deleted)

{com}. drop if q1b==2804 & _index==116
{txt}(1 observation deleted)

{com}. keep q1b q3c1new q3c2new q3e _index
{txt}
{com}. rename q3e q3enew
{res}{txt}
{com}. recode q3enew(2=0)
{txt}(155 changes made to {bf:q3enew})

{com}. destring q1b, replace
{txt}q1b already numeric; no {res}replace
{txt}
{com}. merge 1:1 q1b using "$path_data/temp/followup_student_data"
{res}
{txt}{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}              68
{txt}{col 9}from master{col 30}{res}               1{txt}  (_merge==1)
{col 9}from using{col 30}{res}              67{txt}  (_merge==2)

{col 5}Matched{col 30}{res}             219{txt}  (_merge==3)
{col 5}{hline 41}

{com}. save "$path_data/temp/followup_student_data", replace
{txt}{p 0 4 2}
file {bf}
/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/data/temp/followup_student_data.dta{rm}
saved
{p_end}

{com}. 
. 
. // check the accuracy of q3c
. drop if _merge==1
{txt}(1 observation deleted)

{com}. drop _merge
{txt}
{com}. sum q3c1new

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}q3c1new {c |}{res}         94    5.851064    .6038843          4          7
{txt}
{com}. sum q3c2new

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}q3c2new {c |}{res}         94    9.829787    3.999028          4         21
{txt}
{com}. * tab treatment q3enew
. tab q3e q3enew

           {txt}{c |}          q3e
       q3e {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}        14         11 {txt}{c |}{res}        25 
{txt}         1 {c |}{res}       141         53 {txt}{c |}{res}       194 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       155         64 {txt}{c |}{res}       219 
{txt}
{com}. 
. // merge with baseline & endline
. use "$path_data/original_raw_score", clear
{txt}
{com}. keep student_no DT_score_pre cpcs_pre rosen_pre
{txt}
{com}. save "$path_data/temp/rawscore", replace
{txt}{p 0 4 2}
file {bf}
/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/data/temp/rawscore.dta{rm}
saved
{p_end}

{com}. 
. use "$path_data/temp/followup_student_data", clear
{txt}
{com}. drop _merge 
{txt}
{com}. merge 1:1 student_no using "$path_data/original_main"
{res}{txt}(label {bf:{txt}_merge} already defined)

{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}             812
{txt}{col 9}from master{col 30}{res}              44{txt}  (_merge==1)
{col 9}from using{col 30}{res}             768{txt}  (_merge==2)

{col 5}Matched{col 30}{res}             243{txt}  (_merge==3)
{col 5}{hline 41}

{com}. rename _merge _merge_base_character
{res}{txt}
{com}. merge 1:1 student_no using "$path_data/temp/rawscore"
{res}
{txt}{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}              44
{txt}{col 9}from master{col 30}{res}              44{txt}  (_merge==1)
{col 9}from using{col 30}{res}               0{txt}  (_merge==2)

{col 5}Matched{col 30}{res}           1,011{txt}  (_merge==3)
{col 5}{hline 41}

{com}. rename _merge _merge_base_score
{res}{txt}
{com}. tab treatment _merge_base_score

           {txt}{c |}  Matching
           {c |}   result
           {c |} from merge
 treatment {c |} Matched ( {c |}     Total
{hline 11}{c +}{hline 11}{c +}{hline 10}
         0 {c |}{res}       478 {txt}{c |}{res}       478 
{txt}         1 {c |}{res}       526 {txt}{c |}{res}       526 
{txt}{hline 11}{c +}{hline 11}{c +}{hline 10}
     Total {c |}{res}     1,004 {txt}{c |}{res}     1,004 
{txt}
{com}. 
. gen attrition = 0 if _merge_base_character == 3
{txt}(812 missing values generated)

{com}. recode attrition (.=1)
{txt}(812 changes made to {bf:attrition})

{com}. tab attrition

  {txt}attrition {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        243       23.03       23.03
{txt}          1 {c |}{res}        812       76.97      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,055      100.00
{txt}
{com}. 
. // fix missing values using baseline information
. replace school_no = 18 if school_no == . & student_no == 1817
{txt}(1 real change made)

{com}. replace treatment = 1 if student_no == 1817
{txt}(1 real change made)

{com}. replace grade = 2 if student_no == 1817
{txt}(1 real change made)

{com}. replace branch1 = 0 if student_no == 1817
{txt}(1 real change made)

{com}. replace branch2 = 0 if student_no == 1817
{txt}(1 real change made)

{com}. replace branch3 = 1 if student_no == 1817
{txt}(1 real change made)

{com}. replace branch4 = 0 if student_no == 1817
{txt}(1 real change made)

{com}. 
. save "$path_data/temp/student_unbalance", replace
{txt}{p 0 4 2}
file {bf}
/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/data/temp/student_unbalance.dta{rm}
saved
{p_end}

{com}. 
. 
. // keep balanced panel
. keep if attrition == 0
{txt}(812 observations deleted)

{com}. 
. save "$path_data/temp/endline_followup_student_data", replace
{txt}{p 0 4 2}
file {bf}
/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/data/temp/endline_followup_student_data.dta{rm}
saved
{p_end}

{com}. 
. 
. gen followup_cog = q6a1_correct + q6a2_correct + q6a3a_correct + q6a3b_correct + q6a4_correct + q6a5_correct
{txt}
{com}. 
. /// non-cog
> // positive: 2,3,5,7,10,11,12,17,18,20,21,22,23,25,26,27,28,29,32,33,34,36,37,39
. // positive-cog:1,13,14,19,24,
. // negative: 4,6,8,9,30,31,35,38,40
. 
. local q99 q6c1 q6c2 q6c3 q6c4 q6c5 q6c6 q6c7 q6c8 q6c9 q6c10 q6c11 q6c12 q6c13 q6c14 q6c15 q6c16 q6c17 q6c18 q6c19 q6c20 ///
> q6c21 q6c22 q6c23 q6c24 q6c25 q6c26 q6c27 q6c28 q6c29 q6c30 q6c31 q6c32 q6c33 q6c34 q6c35 q6c36 q6c37 q6c38 q6c39 q6c40 ///
> q8a1a q8a2a q8a3a q8a4a q8a5a
{txt}
{com}. 
. foreach y in `q99'{c -(}
{txt}  2{com}. replace `y'=.  if `y'==99
{txt}  3{com}. {c )-}
{txt}(1 real change made, 1 to missing)
(1 real change made, 1 to missing)
(1 real change made, 1 to missing)
(0 real changes made)
(3 real changes made, 3 to missing)
(2 real changes made, 2 to missing)
(0 real changes made)
(2 real changes made, 2 to missing)
(0 real changes made)
(0 real changes made)
(1 real change made, 1 to missing)
(1 real change made, 1 to missing)
(0 real changes made)
(0 real changes made)
(6 real changes made, 6 to missing)
(8 real changes made, 8 to missing)
(1 real change made, 1 to missing)
(12 real changes made, 12 to missing)
(12 real changes made, 12 to missing)
(5 real changes made, 5 to missing)
(1 real change made, 1 to missing)
(2 real changes made, 2 to missing)
(2 real changes made, 2 to missing)
(7 real changes made, 7 to missing)
(13 real changes made, 13 to missing)
(1 real change made, 1 to missing)
(2 real changes made, 2 to missing)
(1 real change made, 1 to missing)
(8 real changes made, 8 to missing)
(1 real change made, 1 to missing)
(0 real changes made)
(1 real change made, 1 to missing)
(0 real changes made)
(0 real changes made)
(26 real changes made, 26 to missing)
(16 real changes made, 16 to missing)
(0 real changes made)
(0 real changes made)
(1 real change made, 1 to missing)
(20 real changes made, 20 to missing)
(3 real changes made, 3 to missing)
(2 real changes made, 2 to missing)
(4 real changes made, 4 to missing)
(0 real changes made)
(1 real change made, 1 to missing)

{com}. 
. gen noncog4 = 5 - q6c4
{txt}
{com}. gen noncog6 = 5 - q6c6
{txt}(2 missing values generated)

{com}. gen noncog8 = 5 - q6c8
{txt}(2 missing values generated)

{com}. gen noncog9 = 5 - q6c9
{txt}
{com}. gen noncog30 = 5 - q6c30
{txt}(1 missing value generated)

{com}. gen noncog31 = 5 - q6c31
{txt}
{com}. gen noncog35 = 5 - q6c35
{txt}(26 missing values generated)

{com}. gen noncog38 = 5 - q6c38
{txt}
{com}. gen noncog40 = 5 - q6c40
{txt}(20 missing values generated)

{com}. 
. gen followup_noncog = q6c1+q6c2+q6c3+noncog4+q6c5+noncog6+q6c7+noncog8+noncog9+q6c10+q6c11+q6c12+q6c13+q6c14+q6c17+q6c18+q6c19+q6c20+q6c21+q6c22+q6c23+q6c24+q6c25+q6c26+q6c27+q6c28+q6c29+noncog30+noncog31+q6c32+q6c33+q6c34+noncog35+q6c36+q6c37+noncog38+noncog40+q6c39
{txt}(64 missing values generated)

{com}. gen followup_noncog2 = q6c2+q6c3+noncog4+q6c5+noncog6+q6c7+noncog8+noncog9+q6c10+q6c11+q6c12+q6c17+q6c18+q6c20+q6c21+q6c22+q6c23+q6c25+q6c26+q6c27+q6c28+q6c29+noncog30+noncog31+q6c32+q6c33+q6c34+noncog35+q6c36+q6c37+noncog38+noncog40+q6c39
{txt}(63 missing values generated)

{com}. 
. sum followup_noncog followup_noncog2

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
followup_n~g {c |}{res}        179    64.49721    16.11419         31        111
{txt}followup_n~2 {c |}{res}        180    55.78889    14.00558         25         94
{txt}
{com}. 
. replace followup_noncog = 190 - followup_noncog
{txt}(179 real changes made)

{com}. replace followup_noncog2 = 180 - followup_noncog2
{txt}(179 real changes made)

{com}. 
. gen RSES = 40 - q6c2 - q6c3 - noncog4 - noncog6 - noncog8 - noncog9 - q6c10 - q6c11
{txt}(7 missing values generated)

{com}. gen CPCS = 50 - q6c2 - q6c3 - noncog4 - q6c5 - noncog6 -q6c7 - noncog8 - noncog9 - q6c10 - q6c11
{txt}(7 missing values generated)

{com}. 
. /// variables for study situation
> gen tutor = 1 if q3a == 1
{txt}(149 missing values generated)

{com}. gen study_other = 1 if q4a == 1
{txt}(134 missing values generated)

{com}. gen study_affect_covid = 1 if q9a21 == 1
{txt}(90 missing values generated)

{com}. gen hometutoring = 1 if q9a2a1 == 1
{txt}(215 missing values generated)

{com}. gen onlineclass = 1 if q9a2a2 == 1
{txt}(223 missing values generated)

{com}. gen studymyself = 1 if q9a2a3 == 1
{txt}(115 missing values generated)

{com}. gen parentsteach = 1 if q9a2a4 == 1
{txt}(220 missing values generated)

{com}. recode tutor study_other study_affect_covid hometutoring onlineclass studymyself parentsteach (.=0)
{txt}(149 changes made to {bf:tutor})
(134 changes made to {bf:study_other})
(90 changes made to {bf:study_affect_covid})
(215 changes made to {bf:hometutoring})
(223 changes made to {bf:onlineclass})
(115 changes made to {bf:studymyself})
(220 changes made to {bf:parentsteach})

{com}. 
. /// other variable
> gen phone_survey = 1 if q1a0 == 2
{txt}(184 missing values generated)

{com}. recode phone_survey (.=0)
{txt}(184 changes made to {bf:phone_survey})

{com}. 
. /// Standardization
> egen DT_score_pre_mean = mean(DT_score_pre)
{txt}
{com}. egen DT_score_pre_sd = sd(DT_score_pre)
{txt}
{com}. gen DT_score_pre_std = (DT_score_pre-DT_score_pre_mean)/DT_score_pre_sd
{txt}(4 missing values generated)

{com}. drop DT_score_pre_mean DT_score_pre_sd 
{txt}
{com}. 
. egen cpcs_pre_mean = mean(cpcs_pre)
{txt}
{com}. egen cpcs_pre_sd = sd(cpcs_pre)
{txt}
{com}. gen cpcs_pre_std = (cpcs_pre-cpcs_pre_mean)/cpcs_pre_sd
{txt}
{com}. drop cpcs_pre_mean cpcs_pre_sd 
{txt}
{com}. 
. egen rosen_pre_mean = mean(rosen_pre)
{txt}
{com}. egen rosen_pre_sd = sd(rosen_pre)
{txt}
{com}. gen rosen_pre_std = (rosen_pre-rosen_pre_mean)/rosen_pre_sd
{txt}
{com}. drop rosen_pre_mean rosen_pre_sd 
{txt}
{com}. 
. egen followup_cog_mean = mean(followup_cog)
{txt}
{com}. egen followup_cog_sd = sd(followup_cog)
{txt}
{com}. gen followup_cog_std = (followup_cog-followup_cog_mean)/followup_cog_sd
{txt}
{com}. drop followup_cog_mean followup_cog_sd 
{txt}
{com}. 
. egen followup_noncog_mean = mean(followup_noncog)
{txt}
{com}. egen followup_noncog_sd = sd(followup_noncog)
{txt}
{com}. gen followup_noncog_std = (followup_noncog - followup_noncog_mean)/followup_noncog_sd
{txt}(64 missing values generated)

{com}. drop followup_noncog_mean followup_noncog_sd 
{txt}
{com}. 
. egen CPCS_mean = mean(CPCS)
{txt}
{com}. egen CPCS_sd = sd(CPCS)
{txt}
{com}. gen CPCS_std = (CPCS - CPCS_mean)/CPCS_sd
{txt}(7 missing values generated)

{com}. drop CPCS_mean CPCS_sd 
{txt}
{com}. 
. egen RSES_mean = mean(RSES)
{txt}
{com}. egen RSES_sd = sd(RSES)
{txt}
{com}. gen RSES_std = (RSES-RSES_mean)/RSES_sd
{txt}(7 missing values generated)

{com}. drop RSES_mean RSES_sd 
{txt}
{com}. 
. /// missing
> gen DT_score_pre_std_missing_dummy = 1 if DT_score_pre_std == .
{txt}(239 missing values generated)

{com}. gen cpcs_pre_std_missing_dummy = 1 if cpcs_pre_std == .
{txt}(243 missing values generated)

{com}. gen rosen_pre_std_missing_dummy = 1 if rosen_pre_std == .
{txt}(243 missing values generated)

{com}. recode DT_score_pre_std_missing_dummy cpcs_pre_std_missing_dummy rosen_pre_std_missing_dummy (.=0)
{txt}(239 changes made to {bf:DT_score_pre_std_missing_dummy})
(243 changes made to {bf:cpcs_pre_std_missing_dummy})
(243 changes made to {bf:rosen_pre_std_missing_dummy})

{com}. 
. gen DT_score_pre_std_missing_0 = DT_score_pre_std if DT_score_pre_std_missing == 0
{txt}(4 missing values generated)

{com}. gen cpcs_pre_std_missing_0 = cpcs_pre_std if cpcs_pre_std != .
{txt}
{com}. gen rosen_pre_std_missing_0 = rosen_pre_std if rosen_pre_std != .
{txt}
{com}. recode DT_score_pre_std_missing_0 cpcs_pre_std_missing_0 rosen_pre_std_missing_0 (.=0)
{txt}(4 changes made to {bf:DT_score_pre_std_missing_0})
(0 changes made to {bf:cpcs_pre_std_missing_0})
(0 changes made to {bf:rosen_pre_std_missing_0})

{com}. 
. gen hyper = 1 if q7d2a == 1 & q7d2b == 2
{txt}(231 missing values generated)

{com}. replace hyper = 1 if q7d2a == 1 & q7d2b == 3
{txt}(10 real changes made)

{com}. replace hyper = 1 if q7d2a == 2 & q7d2b == 3
{txt}(13 real changes made)

{com}. gen hypernoinfo = 1 if q7d2a == .
{txt}(184 missing values generated)

{com}. recode hyper hypernoinfo (.=0)
{txt}(208 changes made to {bf:hyper})
(184 changes made to {bf:hypernoinfo})

{com}. replace hyper = . if hypernoinfo == 1
{txt}(59 real changes made, 59 to missing)

{com}. 
. 
. 
. /// merge teacher information
> merge 1:1 student_no using "$path_data/temp/teacher"
{res}
{txt}{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}             763
{txt}{col 9}from master{col 30}{res}               1{txt}  (_merge==1)
{col 9}from using{col 30}{res}             762{txt}  (_merge==2)

{col 5}Matched{col 30}{res}             242{txt}  (_merge==3)
{col 5}{hline 41}

{com}. rename _merge _merge_teacher
{res}{txt}
{com}. recode age_tchr(.=0)
{txt}(33 changes made to {bf:age_tchr})

{com}. gen age_tchr_missing_dummy = 1 if age_tchr == 0
{txt}(972 missing values generated)

{com}. recode age_tchr_missing_dummy(.=0)
{txt}(972 changes made to {bf:age_tchr_missing_dummy})

{com}. 
. 
. save "$path_data/temp/followup_student_baseline_score_missing_dummy", replace
{txt}{p 0 4 2}
file {bf}
/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/data/temp/followup_student_baseline_score_missing_dummy.dta{rm}
saved
{p_end}

{com}. 
. 
. 
. 
{txt}end of do-file

{com}. 
. do "$path_do/1_data_cleaning_parents.do"
{txt}
{com}. clear all
{res}{txt}
{com}. set more off
{txt}
{com}. 
. import excel "$path_data/followup_parents_master.xlsx", clear first
{res}{text}(593 vars, 230 obs)

{com}. 
. local q3888999 b3 b7 ///
> cm1_2 cm1_3 cm1_4 cm1_5 cm1_6 cm1_7 cm1_8 cm1_9 cm1_10 cm1_11 ///
> cm2_2 cm2_3 cm2_4 cm2_5 cm2_6 cm2_7 cm2_8 cm2_9 cm2_10 cm2_11 ///
> cm3_2 cm3_3 cm3_4 cm3_5 cm3_6 cm3_7 cm3_8 cm3_9 cm3_10 cm3_11 ///
> cm4_2 cm4_3 cm4_4 cm4_5 cm4_6 cm4_7 cm4_8 cm4_9 cm4_10 cm4_11 ///
> e2 f2_1
{txt}
{com}. 
. local yesno f1_1 f1_3 f2_1
{txt}
{com}. 
. local missingzero e9_1 e9_2 e9_3 e9_4 e9_5 e9_6 e9_7 e9_8 e9_9
{txt}
{com}. 
. foreach y in `q3888999'{c -(}
{txt}  2{com}. replace `y'=.  if `y'==3
{txt}  3{com}. replace `y'=.  if `y'==888
{txt}  4{com}. replace `y'=.  if `y'==999
{txt}  5{com}. {c )-}
{txt}(21 real changes made, 21 to missing)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(1 real change made, 1 to missing)
(3 real changes made, 3 to missing)
(0 real changes made)
(0 real changes made)
(5 real changes made, 5 to missing)
(0 real changes made)
(0 real changes made)
(5 real changes made, 5 to missing)
(0 real changes made)
(3 real changes made, 3 to missing)
(2 real changes made, 2 to missing)
(0 real changes made)
(1 real change made, 1 to missing)
(4 real changes made, 4 to missing)
(0 real changes made)
(4 real changes made, 4 to missing)
(1 real change made, 1 to missing)
(0 real changes made)
(2 real changes made, 2 to missing)
(2 real changes made, 2 to missing)
(0 real changes made)
(2 real changes made, 2 to missing)
(4 real changes made, 4 to missing)
(0 real changes made)
(8 real changes made, 8 to missing)
(5 real changes made, 5 to missing)
(0 real changes made)
(3 real changes made, 3 to missing)
(2 real changes made, 2 to missing)
(0 real changes made)
(0 real changes made)
(1 real change made, 1 to missing)
(0 real changes made)
(1 real change made, 1 to missing)
(3 real changes made, 3 to missing)
(0 real changes made)
(1 real change made, 1 to missing)
(2 real changes made, 2 to missing)
(0 real changes made)
(1 real change made, 1 to missing)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(3 real changes made, 3 to missing)
(0 real changes made)
(2 real changes made, 2 to missing)
(3 real changes made, 3 to missing)
(0 real changes made)
(1 real change made, 1 to missing)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(2 real changes made, 2 to missing)
(0 real changes made)
(4 real changes made, 4 to missing)
(4 real changes made, 4 to missing)
(0 real changes made)
(1 real change made, 1 to missing)
(4 real changes made, 4 to missing)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(1 real change made, 1 to missing)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(1 real change made, 1 to missing)
(1 real change made, 1 to missing)
(2 real changes made, 2 to missing)
(1 real change made, 1 to missing)
(1 real change made, 1 to missing)
(0 real changes made)
(1 real change made, 1 to missing)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(3 real changes made, 3 to missing)
(0 real changes made)
(0 real changes made)
(2 real changes made, 2 to missing)
(0 real changes made)
(2 real changes made, 2 to missing)
(0 real changes made)
(1 real change made, 1 to missing)
(1 real change made, 1 to missing)
(1 real change made, 1 to missing)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(119 real changes made, 119 to missing)
(1 real change made, 1 to missing)
(0 real changes made)
(3 real changes made, 3 to missing)
(0 real changes made)
(0 real changes made)

{com}. 
. foreach y in `yesno'{c -(}
{txt}  2{com}. replace `y'=0  if `y'==2
{txt}  3{com}. {c )-}
{txt}(1 real change made)
(212 real changes made)
(2 real changes made)

{com}. 
. foreach y in `missingzero'{c -(}
{txt}  2{com}. replace `y'=0  if `y'==.
{txt}  3{com}. {c )-}
{txt}(55 real changes made)
(109 real changes made)
(230 real changes made)
(22 real changes made)
(195 real changes made)
(227 real changes made)
(221 real changes made)
(204 real changes made)
(9 real changes made)

{com}. 
. gen hhmember = a3
{txt}
{com}. gen hhheadage = am1_3a if am1_4 == 1
{txt}(2 missing values generated)

{com}. replace hhheadage = am2_3a if am2_4 == 1
{txt}(0 real changes made)

{com}. replace hhheadage = am3_3a if am3_4 == 1
{txt}(2 real changes made)

{com}. replace hhheadage = am4_3a if am4_4 == 1
{txt}(0 real changes made)

{com}. gen hhheadedu = am1_6 if am1_4 == 1
{txt}(2 missing values generated)

{com}. replace hhheadedu = am2_6 if am2_4 == 1
{txt}(0 real changes made)

{com}. replace hhheadedu = am3_6 if am3_4 == 1
{txt}(1 real change made)

{com}. replace hhheadedu = am4_6 if am4_4 == 1
{txt}(0 real changes made)

{com}. 
. gen hhheadeduyear = hhheadedu
{txt}(1 missing value generated)

{com}. replace hhheadeduyear = 10 if hhheadedu == 11
{txt}(1 real change made)

{com}. replace hhheadeduyear = 0 if hhheadedu == 17
{txt}(51 real changes made)

{com}. replace hhheadeduyear = 18 if hhheadedu == 15
{txt}(1 real change made)

{com}. replace hhheadeduyear = . if hhheadedu == 888
{txt}(2 real changes made, 2 to missing)

{com}. replace hhheadeduyear = . if hhheadedu == 999
{txt}(1 real change made, 1 to missing)

{com}. 
. destring x1d x1f x1h , replace
{txt}x1d: all characters numeric; {res}replaced {txt}as {res}int
{txt}x1f: all characters numeric; {res}replaced {txt}as {res}long
{txt}(167 missing values generated)
{res}{txt}x1h: all characters numeric; {res}replaced {txt}as {res}int
{txt}(228 missing values generated)
{res}{txt}
{com}. 
. keep x1d x1f x1h hhmember hhheadage hhheadedu hhheadeduyear
{txt}
{com}. 
. preserve
{txt}
{com}. collapse (mean) hhmember hhheadage hhheadedu hhheadeduyear, by(x1d)
{res}{txt}
{com}. replace hhheadeduyear = . if hhheadeduyear == 4.5
{txt}(1 real change made, 1 to missing)

{com}. rename x1d student_no
{res}{txt}
{com}. save "$path_data/temp/endline_followup_parents_data_1stchild", replace
{txt}{p 0 4 2}
file {bf}
/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/data/temp/endline_followup_parents_data_1stchild.dta{rm}
saved
{p_end}

{com}. 
. restore
{txt}
{com}. preserve
{txt}
{com}. rename x1f student_no
{res}{txt}
{com}. drop if student_no == .
{txt}(167 observations deleted)

{com}. collapse (mean) hhmember hhheadage hhheadedu hhheadeduyear, by(student_no)
{res}{txt}
{com}. replace hhheadeduyear = . if hhheadeduyear == 4.5
{txt}(1 real change made, 1 to missing)

{com}. save "$path_data/temp/endline_followup_parents_data_2ndchild", replace
{txt}{p 0 4 2}
file {bf}
/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/data/temp/endline_followup_parents_data_2ndchild.dta{rm}
saved
{p_end}

{com}. 
. restore
{txt}
{com}. preserve
{txt}
{com}. rename x1h student_no
{res}{txt}
{com}. drop if student_no == .
{txt}(228 observations deleted)

{com}. collapse (mean) hhmember hhheadage hhheadedu hhheadeduyear, by(student_no)
{res}{txt}
{com}. replace hhheadeduyear = . if hhheadeduyear == 4.5
{txt}(0 real changes made)

{com}. save "$path_data/temp/endline_followup_parents_data_3rdchild", replace
{txt}{p 0 4 2}
file {bf}
/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/data/temp/endline_followup_parents_data_3rdchild.dta{rm}
saved
{p_end}

{com}. 
. 
. 
{txt}end of do-file

{com}. 
. do "$path_do/1_data_cleaning_merge.do"
{txt}
{com}. set more off
{txt}
{com}. clear all
{res}{txt}
{com}. 
. use "$path_data/temp/followup_student_baseline_score_missing_dummy", clear
{txt}
{com}. merge 1:1 student_no using "$path_data/temp/endline_followup_parents_data_1stchild"
{res}
{txt}{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}             789
{txt}{col 9}from master{col 30}{res}             783{txt}  (_merge==1)
{col 9}from using{col 30}{res}               6{txt}  (_merge==2)

{col 5}Matched{col 30}{res}             222{txt}  (_merge==3)
{col 5}{hline 41}

{com}. rename _merge _merge_1st
{res}{txt}
{com}. rename hhmember hhmember_1st
{res}{txt}
{com}. rename hhheadage hhheadage_1st
{res}{txt}
{com}. rename hhheadeduyear hhheadeduyear_1st
{res}{txt}
{com}. 
. merge 1:1 student_no using "$path_data/temp/endline_followup_parents_data_2ndchild"
{res}{txt}{p 0 7 2}
(variable
{bf:student_no} was {bf:float}, now {bf:double} to accommodate using data's values)
{p_end}

{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}           1,025
{txt}{col 9}from master{col 30}{res}             987{txt}  (_merge==1)
{col 9}from using{col 30}{res}              38{txt}  (_merge==2)

{col 5}Matched{col 30}{res}              24{txt}  (_merge==3)
{col 5}{hline 41}

{com}. rename _merge _merge_2nd
{res}{txt}
{com}. rename hhmember hhmember_2nd
{res}{txt}
{com}. rename hhheadage hhheadage_2nd
{res}{txt}
{com}. rename hhheadeduyear hhheadeduyear_2nd
{res}{txt}
{com}. 
. merge 1:1 student_no using "$path_data/temp/endline_followup_parents_data_3rdchild"
{res}
{txt}{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}           1,047
{txt}{col 9}from master{col 30}{res}           1,047{txt}  (_merge==1)
{col 9}from using{col 30}{res}               0{txt}  (_merge==2)

{col 5}Matched{col 30}{res}               2{txt}  (_merge==3)
{col 5}{hline 41}

{com}. rename _merge _merge_3rd
{res}{txt}
{com}. rename hhmember hhmember_3rd
{res}{txt}
{com}. rename hhheadage hhheadage_3rd
{res}{txt}
{com}. rename hhheadeduyear hhheadeduyear_3rd
{res}{txt}
{com}. 
. recode hhmember* hhheadage* hhheadeduyear* (. = 0) 
{txt}(821 changes made to {bf:hhmember_1st})
(987 changes made to {bf:hhmember_2nd})
(1,047 changes made to {bf:hhmember_3rd})
(822 changes made to {bf:hhheadage_1st})
(988 changes made to {bf:hhheadage_2nd})
(1,047 changes made to {bf:hhheadage_3rd})
(826 changes made to {bf:hhheadeduyear_1st})
(991 changes made to {bf:hhheadeduyear_2nd})
(1,047 changes made to {bf:hhheadeduyear_3rd})

{com}. 
. gen hhmember = hhmember_1st + hhmember_2nd + hhmember_3rd
{txt}
{com}. gen hhheadage = hhheadage_1st + hhheadage_2nd + hhheadage_3rd
{txt}
{com}. gen hhheadeduyear = hhheadeduyear_1st + hhheadeduyear_2nd + hhheadeduyear_3rd
{txt}
{com}. 
. keep if attrition == 0
{txt}(806 observations deleted)

{com}. 
. save "$path_data/temp/followup_student_parents_matched", replace
{txt}{p 0 4 2}
file {bf}
/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/data/temp/followup_student_parents_matched.dta{rm}
saved
{p_end}

{com}. 
{txt}end of do-file

{com}. 
. *** run the codes for outputs.
. 
. 
. do "$path_do/2_table_1.do"
{txt}
{com}. * This is the do file to create "Table 1. Summary Statistics"
. set seed 123
{txt}
{com}. 
. use "$path_data/temp/followup_student_parents_matched", clear
{txt}
{com}. 
. corr rosen_pre_std cpcs_pre_std
{txt}(obs=243)

             {c |} rosen_~d cpcs_p~d
{hline 13}{c +}{hline 18}
rosen_pre_~d {c |}{res}   1.0000
{txt}cpcs_pre_std {c |}{res}   0.9026   1.0000

{txt}
{com}. corr RSES_std CPCS_std
{txt}(obs=236)

             {c |} RSES_std CPCS_std
{hline 13}{c +}{hline 18}
    RSES_std {c |}{res}   1.0000
    {txt}CPCS_std {c |}{res}   0.9701   1.0000

{txt}
{com}. 
. 
. /// Varable Selection
> /// Baseline
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat DT_score_pre_std rosen_pre_std cpcs_pre_std if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_bl = r(StatTotal)
{txt}  5{com}. 
. tabstat DT_score_pre_std rosen_pre_std cpcs_pre_std if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_bl = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}      144       145       145
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   DT_score_p~d  rosen_pre_~d  cpcs_pre_std
N {res}          144           145           145
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}       95        98        98
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   DT_score_p~d  rosen_pre_~d  cpcs_pre_std
N {res}           95            98            98
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res}-.0313509  .0382918  .1345164
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      DT_score_p~d  rosen_pre_~d  cpcs_pre_std
Mean {res}   -.03135095     .03829184      .1345164
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .0475214 -.0566567 -.1990291
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      DT_score_p~d  rosen_pre_~d  cpcs_pre_std
Mean {res}    .04752144    -.05665667    -.19902912
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} 1.023177  .9748496  .9271749
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    DT_score_p~d  rosen_pre_~d  cpcs_pre_std
SD {res}    1.0231772     .97484957     .92717486
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} .9672202  1.038561  1.073121
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    DT_score_p~d  rosen_pre_~d  cpcs_pre_std
SD {res}    .96722024      1.038561     1.0731214
{reset}
{com}. 
. matrix n_bl = J(1,3,.)
{txt}
{com}. forvalues i = 1/3 {c -(}
{txt}  2{com}.         matrix n_bl[1,`i'] = n_tr_bl[1,`i'] + n_ct_bl[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in DT_score_pre_std rosen_pre_std cpcs_pre_std{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}239
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 32
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  2
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.5
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        DT_score_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0788724{col 38}{space 1}  -0.38{col 46}{space 3}0.726{col 54}{space 3}-.5170202{col 66}{space 3} .3849625
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}           rosen_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0949485{col 38}{space 1}   0.47{col 46}{space 3}0.600{col 54}{space 3}-.3528281{col 66}{space 3} .5243389
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}            cpcs_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3335455{col 38}{space 1}   1.82{col 46}{space 3}0.116{col 54}{space 3}-.0763347{col 66}{space 3} .7082129
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. /// Family
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat hhmember hhheadage hhheadeduyear if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_parent = r(StatTotal)
{txt}  5{com}. 
. tabstat hhmember hhheadage hhheadeduyear if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_parent = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}      145       145       145
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
       hhmember     hhheadage  hhheadeduy~r
N {res}          145           145           145
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}       98        98        98
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
       hhmember     hhheadage  hhheadeduy~r
N {res}           98            98            98
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res} 4.510345  46.57241  2.331034
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
          hhmember     hhheadage  hhheadeduy~r
Mean {res}    4.5103448     46.572414     2.3310345
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res} 4.265306  46.68878  3.163265
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
          hhmember     hhheadage  hhheadeduy~r
Mean {res}    4.2653061     46.688776     3.1632653
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} 1.280827   9.03907  2.995495
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
        hhmember     hhheadage  hhheadeduy~r
SD {res}    1.2808268     9.0390702     2.9954947
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} 1.197515  9.408681  3.530993
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
        hhmember     hhheadage  hhheadeduy~r
SD {res}    1.1975148     9.4086808     3.5309935
{reset}
{com}. 
. matrix n_parent = J(1,3,.)
{txt}
{com}. forvalues i = 1/3 {c -(}
{txt}  2{com}.         matrix n_parent[1,`i'] = n_tr_parent[1,`i'] + n_ct_parent[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in hhmember hhheadage hhheadeduyear{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                hhmember{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2450387{col 38}{space 1}   1.29{col 46}{space 3}0.190{col 54}{space 3}-.1378142{col 66}{space 3} .7032024
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}               hhheadage{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.1163617{col 38}{space 1}  -0.07{col 46}{space 3}0.914{col 54}{space 3}-3.252753{col 66}{space 3} 3.440111
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}           hhheadeduyear{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.8322308{col 38}{space 1}  -2.22{col 46}{space 3}0.018{col 54}{space 3}-1.589777{col 66}{space 3}-.0728916
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. 
. 
. /// School　attendance
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat q2a q2b q2c q2h if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_school = r(StatTotal)
{txt}  5{com}. 
. tabstat q2a q2b q2c q2h if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_school = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:N} {...}
{c |}{...}
 {res}      145       145       145       145
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
   q2a  q2b  q2c  q2h
N {res} 145  145  145  145
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:N} {...}
{c |}{...}
 {res}       98        98        98        98
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
   q2a  q2b  q2c  q2h
N {res}  98   98   98   98
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .5517241  9.606897   .062069  .3793103
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
            q2a        q2b        q2c        q2h
Mean {res} .55172414  9.6068966  .06206897  .37931034
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .5306122  9.602041  .0408163  .4489796
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
            q2a        q2b        q2c        q2h
Mean {res} .53061224  9.6020408  .04081633  .44897959
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:SD} {...}
{c |}{...}
 {res} .4990412  1.029405  .2421171  .4868973
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
          q2a        q2b        q2c        q2h
SD {res} .49904123  1.0294048   .2421171  .48689728
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:SD} {...}
{c |}{...}
 {res} .5016279  .8703571  .1988818  .4999474
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
          q2a        q2b        q2c        q2h
SD {res}  .5016279  .87035715  .19888179   .4999474
{reset}
{com}. 
. matrix n_school = J(1,4,.)
{txt}
{com}. forvalues i = 1/4 {c -(}
{txt}  2{com}.         matrix n_school[1,`i'] = n_tr_school[1,`i'] + n_ct_school[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in q2a q2b q2c q2h{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                     q2a{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0211119{col 38}{space 1}   0.25{col 46}{space 3}0.868{col 54}{space 3}-.1592541{col 66}{space 3} .2083223
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                     q2b{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0048557{col 38}{space 1}   0.03{col 46}{space 3}0.990{col 54}{space 3}-.3899922{col 66}{space 3}  .336345
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                     q2c{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0212526{col 38}{space 1}   0.56{col 46}{space 3}0.662{col 54}{space 3}-.0533022{col 66}{space 3} .0982309
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                     q2h{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0696692{col 38}{space 1}  -0.85{col 46}{space 3}0.446{col 54}{space 3}-.2494278{col 66}{space 3} .1087737
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. /// Other study variable
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat tutor study_other study_affect_covid hometutoring onlineclass studymyself parentsteach if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_study = r(StatTotal)
{txt}  5{com}. 
. tabstat tutor study_other study_affect_covid hometutoring onlineclass studymyself parentsteach if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_study = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:N} {...}
{c |}{...}
 {res}      145       145       145       145       145       145       145
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
          tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
N {res}          145           145           145           145           145           145           145
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:N} {...}
{c |}{...}
 {res}       98        98        98        98        98        98        98
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
          tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
N {res}           98            98            98            98            98            98            98
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:Mean} {...}
{c |}{...}
 {res}  .337931   .462069  .6482759  .0965517  .0482759  .5241379  .0275862
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
             tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
Mean {res}    .33793103     .46206897     .64827586     .09655172     .04827586     .52413793     .02758621
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .4591837  .4285714  .6020408  .1428571  .1326531  .5306122  .1938776
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
             tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
Mean {res}    .45918367     .42857143     .60204082     .14285714     .13265306     .53061224     .19387755
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:SD} {...}
{c |}{...}
 {res} .4746445  .5002873  .4791635  .2963701  .2150915  .5011481  .1643517
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
           tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
SD {res}    .47464445     .50028727     .47916354     .29637012     .21509153     .50114811     .16435174
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:SD} {...}
{c |}{...}
 {res} .5008934   .497416  .4919935  .3517262  .3409434  .5016279  .3973667
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
           tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
SD {res}    .50089337       .497416     .49199354     .35172623     .34094336      .5016279     .39736667
{reset}
{com}. 
. matrix n_study = J(1,8,.)
{txt}
{com}. forvalues i = 1/8 {c -(}
{txt}  2{com}.         matrix n_study[1,`i'] = n_tr_study[1,`i'] + n_ct_study[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in tutor study_other study_affect_covid hometutoring onlineclass studymyself parentsteach{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                   tutor{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.1212526{col 38}{space 1}  -1.69{col 46}{space 3}0.120{col 54}{space 3}-.2773205{col 66}{space 3} .0421174
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}             study_other{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0334975{col 38}{space 1}   0.39{col 46}{space 3}0.742{col 54}{space 3}-.1488081{col 66}{space 3} .2186319
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}      study_affect_covid{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  .046235{col 38}{space 1}   0.56{col 46}{space 3}0.576{col 54}{space 3} -.124707{col 66}{space 3} .2364771
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}            hometutoring{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0463054{col 38}{space 1}  -1.11{col 46}{space 3}0.312{col 54}{space 3} -.128302{col 66}{space 3} .0430425
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}             onlineclass{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0843772{col 38}{space 1}  -1.92{col 46}{space 3}0.096{col 54}{space 3} -.179885{col 66}{space 3} .0148069
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}             studymyself{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0064743{col 38}{space 1}  -0.08{col 46}{space 3}0.940{col 54}{space 3}-.1754887{col 66}{space 3} .1699468
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}            parentsteach{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.1662913{col 38}{space 1}  -3.85{col 46}{space 3}0.000{col 54}{space 3} -.255542{col 66}{space 3}-.0629773
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. /// Cognitive
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat followup_cog_std if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_cog = r(StatTotal)
{txt}  5{com}. 
. tabstat followup_cog_std if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_cog = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}{ralign 12:Variable} {...}
{c |}         N
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res}      145
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
   followup_c~d
N {res}          145
{reset}
{ralign 12:Variable} {...}
{c |}         N
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res}       98
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
   followup_c~d
N {res}           98
{reset}
{ralign 12:Variable} {...}
{c |}      Mean
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res}-.0920409
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
      followup_c~d
Mean {res}   -.09204085
{reset}
{ralign 12:Variable} {...}
{c |}      Mean
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res} .1361831
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
      followup_c~d
Mean {res}    .13618309
{reset}
{ralign 12:Variable} {...}
{c |}        SD
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res} 1.070796
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
    followup_c~d
SD {res}     1.070796
{reset}
{ralign 12:Variable} {...}
{c |}        SD
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res} .8725076
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
    followup_c~d
SD {res}    .87250763
{reset}
{com}. 
. matrix n_cog = J(1,1,.)
{txt}
{com}. forvalues i = 1/1 {c -(}
{txt}  2{com}.         matrix n_cog[1,`i'] = n_tr_cog[1,`i'] + n_ct_cog[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2282239{col 38}{space 1}  -1.36{col 46}{space 3}0.192{col 54}{space 3}-.5930653{col 66}{space 3} .1293723
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}.         
. matrix r2_followup_cog_std_temp = r(table)
{txt}
{com}. 
. 
. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix r2_followup_cog_std_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix r2_followup_cog_std_mean[1,`j'] = r2_followup_cog_std_temp[1,`j']
{txt}  3{com}. * standard error
. * matrix r2_followup_cog_std_se[1,`j'] = r2_followup_cog_std_temp[2,`j']
. * p value
. matrix r2_followup_cog_std_pv[1,`j'] = r2_followup_cog_std_temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}.     
. /// Non cognitive
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat followup_noncog_std RSES_std CPCS_std if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_noncog = r(StatTotal)
{txt}  5{com}. 
. tabstat followup_noncog_std RSES_std CPCS_std if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_noncog = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}      105       140       140
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   followup_n~d      RSES_std      CPCS_std
N {res}          105           140           140
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}       74        96        96
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   followup_n~d      RSES_std      CPCS_std
N {res}           74            96            96
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .1969319  .1591241  .1745941
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      followup_n~d      RSES_std      CPCS_std
Mean {res}    .19693189      .1591241     .17459415
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res}-.2794302 -.2320565  -.254617
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      followup_n~d      RSES_std      CPCS_std
Mean {res}   -.27943024    -.23205648    -.25461705
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} 1.006158  1.022691  1.008304
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    followup_n~d      RSES_std      CPCS_std
SD {res}    1.0061577     1.0226907     1.0083041
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} .9279901  .9228443  .9357831
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    followup_n~d      RSES_std      CPCS_std
SD {res}    .92799012     .92284427     .93578307
{reset}
{com}. 
. matrix n_noncog = J(1,3,.)
{txt}
{com}. forvalues i = 1/3 {c -(}
{txt}  2{com}.         matrix n_noncog[1,`i'] = n_tr_noncog[1,`i'] + n_ct_noncog[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in followup_noncog_std RSES_std CPCS_std{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}179
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 32
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}5.6
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}     followup_noncog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .4763621{col 38}{space 1}   2.08{col 46}{space 3}0.074{col 54}{space 3}-.0489589{col 66}{space 3} .9729653
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}236
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.2
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3911806{col 38}{space 1}   2.02{col 46}{space 3}0.064{col 54}{space 3}-.0321475{col 66}{space 3} .7806997
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}236
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.2
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .4292112{col 38}{space 1}   2.26{col 46}{space 3}0.038{col 54}{space 3} .0238533{col 66}{space 3} .7985476
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. 
. /// Behavioral
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat hyper if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_hyper = r(StatTotal)
{txt}  5{com}. 
. tabstat hyper if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_hyper = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}{ralign 12:Variable} {...}
{c |}         N
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res}      113
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
   hyper
N {res}   113
{reset}
{ralign 12:Variable} {...}
{c |}         N
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res}       71
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
   hyper
N {res}    71
{reset}
{ralign 12:Variable} {...}
{c |}      Mean
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res} .2654867
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
          hyper
Mean {res} .26548673
{reset}
{ralign 12:Variable} {...}
{c |}      Mean
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res} .0704225
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
          hyper
Mean {res} .07042254
{reset}
{ralign 12:Variable} {...}
{c |}        SD
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res}  .443559
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
        hyper
SD {res} .44355905
{reset}
{ralign 12:Variable} {...}
{c |}        SD
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res} .2576789
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
        hyper
SD {res} .25767885
{reset}
{com}. 
. matrix n_hyper = J(1,1,.)
{txt}
{com}. forvalues i = 1/1 {c -(}
{txt}  2{com}.         matrix n_hyper[1,`i'] = n_tr_hyper[1,`i'] + n_ct_hyper[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in hyper{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment if hypernoinfo == 0, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}184
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}5.6
{col 69}{txt}max{col 72} = {res} 12
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                   hyper{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1950642{col 38}{space 1}   3.37{col 46}{space 3}0.010{col 54}{space 3} .0645109{col 66}{space 3} .3241339
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. 
. // significant level
. 
. local outcome DT_score_pre_std rosen_pre_std cpcs_pre_std hhmember hhheadage hhheadeduyear q2a q2c q2h tutor study_other followup_cog_std RSES_std CPCS_std
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}.                 if r2_`dep'_pv[1,1]<=0.01 {c -(}
{txt}  3{com}.                         local star_`dep' %3s "***"
{txt}  4{com}.                 {c )-}
{txt}  5{com}.                 else if (r2_`dep'_pv[1,1]>0.01) & (r2_`dep'_pv[1,1]<=0.05) {c -(}
{txt}  6{com}.                         local star_`dep' %2s "**"
{txt}  7{com}.                 {c )-}
{txt}  8{com}.                 else if (r2_`dep'_pv[1,1]>0.05) & (r2_`dep'_pv[1,1]<=0.10) {c -(}
{txt}  9{com}.                         local star_`dep' %1s "*"
{txt} 10{com}.                 {c )-}
{txt} 11{com}.                 else {c -(}
{txt} 12{com}.                         local star_`dep'  ""
{txt} 13{com}.                 {c )-}
{txt} 14{com}. {c )-} 
{txt}
{com}. 
. rwolf DT_score_pre_std rosen_pre_std cpcs_pre_std hhmember hhheadage hhheadeduyear, indepvar(treatment) reps(1000)
Bootstrap replications (1000). This may take some time.
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Romano-Wolf step-down adjusted p-values


Independent variable:  treatment
Outcome variables:   DT_score_pre_std rosen_pre_std cpcs_pre_std hhmember
{col 22}hhheadage hhheadeduyear
Number of resamples: 1000


{hline 78}
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
{hline 20}+{hline 57}
   {txt}DT_score_pre_std {c |}    {res}0.5518             0.5564              0.8472
      {txt}rosen_pre_std {c |}    {res}0.4689             0.4515              0.8472
       {txt}cpcs_pre_std {c |}    {res}0.0105             0.0250              0.0629
           {txt}hhmember {c |}    {res}0.1345             0.1279              0.4206
          {txt}hhheadage {c |}    {res}0.9229             0.9251              0.9251
      {txt}hhheadeduyear {c |}    {res}0.0494             0.0500              0.2238
{hline 78}
{txt}
{com}. rwolf q2a q2c q2h tutor study_other followup_cog_std RSES_std CPCS_std, indepvar(treatment) reps(1000)
Bootstrap replications (1000). This may take some time.
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Romano-Wolf step-down adjusted p-values


Independent variable:  treatment
Outcome variables:   q2a q2c q2h tutor study_other followup_cog_std RSES_std CPCS_std
Number of resamples: 1000


{hline 78}
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
{hline 20}+{hline 57}
                {txt}q2a {c |}    {res}0.7471             0.7383              0.7862
                {txt}q2c {c |}    {res}0.4722             0.4406              0.7862
                {txt}q2h {c |}    {res}0.2801             0.2957              0.6174
              {txt}tutor {c |}    {res}0.0573             0.0480              0.2328
        {txt}study_other {c |}    {res}0.6083             0.5774              0.7862
   {txt}followup_cog_std {c |}    {res}0.0809             0.0669              0.2637
           {txt}RSES_std {c |}    {res}0.0030             0.0020              0.0180
           {txt}CPCS_std {c |}    {res}0.0011             0.0020              0.0110
{hline 78}
{txt}
{com}. 
. 
. /// Table
> tempname hh2
{txt}
{com}. file open `hh2' using "$path_output/summary_stat.tex", write replace
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "% Author: Kazuma Takakura" _newline
{txt}
{com}. file write `hh2' "% Date: `c(current_date)'" _newline
{txt}
{com}. file write `hh2' "% Time: `c(current_time)'" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. 
. file write `hh2' "\begin{c -(}table{c )-}[h!]\footnotesize" _newline
{txt}
{com}. file write `hh2' "  \centering" _newline
{txt}
{com}. file write `hh2' "  \caption{c -(}Summary Statistics{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:sumstat{c )-}" _newline
{txt}
{com}. file write `hh2' "\scalebox{c -(}1{c )-}{c -(}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}threeparttable{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "\begin{c -(}tabular{c )-}{c -(}lccccc{c )-}\toprule" _newline
{txt}
{com}. 
.   
. file write `hh2' " Dependent Variable & Treatment &  Control  & Difference & N   \\\midrule\midrule" _newline
{txt}
{com}. file write `hh2' " Panel A: Baseline & & & &   \\ " _newline
{txt}
{com}. file write `hh2' " DT score^{c -(}a{c )-} & " %04.3f (mean_tr_bl[1,1]) " & " %04.3f (mean_ct_bl[1,1]) " & " %04.3f (r2_DT_score_pre_std_mean[1,1]) `star_DT_score_pre_std' " & " (n_bl[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_bl[1,1]) " ] & [ " %04.3f (sd_ct_bl[1,1]) " ] & ( " %04.3f (r2_DT_score_pre_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.831) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. 
. file write `hh2' " RSES score^{c -(}a{c )-} & " %04.3f (mean_tr_bl[1,2]) " & " %04.3f (mean_ct_bl[1,2]) " & " %04.3f (r2_rosen_pre_std_mean[1,1]) `star_rosen_pre_std' " & "  (n_bl[1,2]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_bl[1,2]) " ] & [ " %04.3f (sd_ct_bl[1,2]) " ] & ( " %04.3f (r2_rosen_pre_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.831) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " CPCS score^{c -(}a{c )-} & " %04.3f (mean_tr_bl[1,3]) " & " %04.3f (mean_ct_bl[1,3]) " & " %04.3f (r2_cpcs_pre_std_mean[1,1]) `star_cpcs_pre_std' " & "  (n_bl[1,3]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_bl[1,3]) " ] & [ " %04.3f (sd_ct_bl[1,3]) " ] & ( " %04.3f (r2_cpcs_pre_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.059) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Household size & " %04.3f (mean_tr_parent[1,1]) " & " %04.3f (mean_ct_parent[1,1]) " & " %04.3f (r2_hhmember_mean[1,1]) `star_hhmember'  " & " (n_parent[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_parent[1,1]) " ] & [ " %04.3f (sd_ct_parent[1,1]) " ] & ( " %04.3f (r2_hhmember_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.464) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Household head age & " %04.3f (mean_tr_parent[1,2]) " & " %04.3f (mean_ct_parent[1,2]) " & " %04.3f (r2_hhheadage_mean[1,1]) `star_hhheadage' " & "  (n_parent[1,2]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_parent[1,2]) " ] & [ " %04.3f (sd_ct_parent[1,2]) " ] & ( " %04.3f (r2_hhheadage_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.920) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Household head education & " %04.3f (mean_tr_parent[1,3]) " & " %04.3f (mean_ct_parent[1,3]) " & " %04.3f (r2_hhheadeduyear_mean[1,1]) `star_hhheadeduyear' " & "  (n_parent[1,3]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_parent[1,3]) " ] & [ " %04.3f (sd_ct_parent[1,3]) " ] & ( " %04.3f (r2_hhheadeduyear_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.248) " \{c )-} &   \\ " _newline
{txt}
{com}. file write `hh2' " \\ "_newline
{txt}
{com}. 
. file write `hh2' " Panel B: Follow-up & & & &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " School attendance & " %04.3f (mean_tr_school[1,1]) " & " %04.3f (mean_ct_school[1,1]) " & " %04.3f (r2_q2a_mean[1,1]) `star_q2a' " & " (n_school[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_school[1,1]) " ] & [ " %04.3f (sd_ct_school[1,1]) " ] & ( " %04.3f (r2_q2a_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.787) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Grade repeat & " %04.3f (mean_tr_school[1,3]) " & " %04.3f (mean_ct_school[1,3]) " & " %04.3f (r2_q2c_mean[1,1]) `star_q2c' " & "  (n_school[1,3]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_school[1,3]) " ] & [ " %04.3f (sd_ct_school[1,3]) " ] & ( " %04.3f (r2_q2c_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.787) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Drop out & " %04.3f (mean_tr_school[1,4]) " & " %04.3f (mean_ct_school[1,4]) " & " %04.3f (r2_q2h_mean[1,1]) `star_q2h'  " & "  (n_school[1,4]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_school[1,4]) " ] & [ " %04.3f (sd_ct_school[1,4]) " ] & ( " %04.3f (r2_q2h_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.576) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Tutoring & " %04.3f (mean_tr_study[1,1]) " & " %04.3f (mean_ct_study[1,1]) " & " %04.3f (r2_tutor_mean[1,1]) `star_tutor'  " & " (n_study[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_study[1,1]) " ] & [ " %04.3f (sd_ct_study[1,1]) " ] & ( " %04.3f (r2_tutor_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.230) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Self-study & " %04.3f (mean_tr_study[1,2]) " & " %04.3f (mean_ct_study[1,2]) " & " %04.3f (r2_study_other_mean[1,1]) `star_study_other' " & "  (n_study[1,2]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_study[1,2]) " ] & [ " %04.3f (sd_ct_study[1,2]) " ] & ( " %04.3f (r2_study_other_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.787) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Rapid math test score^{c -(}a{c )-} & " %04.3f (mean_tr_cog[1,1]) " & " %04.3f (mean_ct_cog[1,1]) " & " %04.3f (r2_followup_cog_std_mean[1,1]) `star_followup_cog_std'  "  & "  (n_cog[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_cog[1,1]) " ] & [ " %04.3f (sd_ct_cog[1,1]) " ] & ( " %04.3f (r2_followup_cog_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.270) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " RSES score^{c -(}a{c )-} & " %04.3f (mean_tr_noncog[1,2]) " & " %04.3f (mean_ct_noncog[1,2]) " & " %04.3f (r2_RSES_std_mean[1,1])   `star_RSES_std' " & " (n_noncog[1,2]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_noncog[1,2]) " ] & [ " %04.3f (sd_ct_noncog[1,2]) " ] & ( " %04.3f (r2_RSES_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.011) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " CPCS score^{c -(}a{c )-} & " %04.3f (mean_tr_noncog[1,3]) " & " %04.3f (mean_ct_noncog[1,3]) " & " %04.3f (r2_CPCS_std_mean[1,1])   `star_CPCS_std' "&  " (n_noncog[1,3]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_noncog[1,3]) " ] & [ " %04.3f (sd_ct_noncog[1,3]) " ] & ( " %04.3f (r2_CPCS_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.006) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. 
. file write `hh2' "\midrule" _newline
{txt}
{com}. file write `hh2' "\end{c -(}tabular{c )-}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\item (a) Variables are standardized using the average and variance of the whole follow-up sample in the February 2022 survey. " _newline
{txt}
{com}. file write `hh2' "\item (b) Standard deviations are reported in square brackets.  " _newline
{txt}
{com}. file write `hh2' "\item (c) Wild clustered bootstrap p-values are reported within parentheses. Clusters are schools at the baseline. There are 34 clusters. " _newline
{txt}
{com}. file write `hh2' "\item (d) Romano-Wolf multiple hypothesis testing p-values are reported in curly brackets. This test is conducted separately for the baseline variables and the follow-up variables." _newline
{txt}
{com}. file write `hh2' "\item (e) Statistical significance is indicated by stars based on the wild clustered bootstrap p-values reported in parentheses: $*$ denotes significance at the 10\% level, $∗∗$ at the 5\% level, and $∗∗∗$ at the 1\% level.  " _newline
{txt}
{com}. file write `hh2' "\end{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}threeparttable{c )-}" _newline
{txt}
{com}. file write `hh2' "{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}table{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. file close `hh2'
{txt}
{com}. 
{txt}end of do-file

{com}. 
. do "$path_do/2_table_2.do"
{txt}
{com}. * This is the do file to create "Table 2. Internal Validity in the Presence of Attrition"
. set seed 123
{txt}
{com}. 
. use "$path_data/temp/student_unbalance", clear
{txt}
{com}. 
. gen female = 1 if student_gender == 0
{txt}(468 missing values generated)

{com}. gen grade_2 = 1 if grade == 2
{txt}(472 missing values generated)

{com}. recode female grade_2(.=0)
{txt}(468 changes made to {bf:female})
(472 changes made to {bf:grade_2})

{com}. 
. gen tracked = 1 - attrition
{txt}
{com}. 
. /// Standardization
> egen DT_score_pre_mean = mean(DT_score_pre)
{txt}
{com}. egen DT_score_pre_sd = sd(DT_score_pre)
{txt}
{com}. gen DT_score_pre_std = (DT_score_pre-DT_score_pre_mean)/DT_score_pre_sd
{txt}(87 missing values generated)

{com}. drop DT_score_pre_mean DT_score_pre_sd 
{txt}
{com}. 
. egen cpcs_pre_mean = mean(cpcs_pre)
{txt}
{com}. egen cpcs_pre_sd = sd(cpcs_pre)
{txt}
{com}. gen cpcs_pre_std = (cpcs_pre-cpcs_pre_mean)/cpcs_pre_sd
{txt}(44 missing values generated)

{com}. drop cpcs_pre_mean cpcs_pre_sd 
{txt}
{com}. 
. egen rosen_pre_mean = mean(rosen_pre)
{txt}
{com}. egen rosen_pre_sd = sd(rosen_pre)
{txt}
{com}. gen rosen_pre_std = (rosen_pre-rosen_pre_mean)/rosen_pre_sd
{txt}(44 missing values generated)

{com}. drop rosen_pre_mean rosen_pre_sd 
{txt}
{com}. 
. 
. replace school_no = 999 if school_no ==.
{txt}variable {bf}{res}school_no{sf}{txt} was {bf}{res}byte{sf}{txt} now {bf}{res}int{sf}
{txt}(50 real changes made)

{com}. 
. /// Group indicators
> gen group = 0 if treatment == 1 & attrition == 0
{txt}(910 missing values generated)

{com}. replace group = 1 if treatment == 0 & attrition == 0
{txt}(98 real changes made)

{com}. replace group = 2 if treatment == 1 & attrition == 1
{txt}(382 real changes made)

{com}. replace group = 3 if treatment == 0 & attrition == 1
{txt}(380 real changes made)

{com}. 
. 
. /// Mean difference
> foreach j in mean sd {c -(}
{txt}  2{com}. tabstat DT_score_pre_std rosen_pre_std cpcs_pre_std female grade_2 if group == 0, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_TR = r(StatTotal)
{txt}  5{com}. 
. tabstat DT_score_pre_std rosen_pre_std cpcs_pre_std female grade_2 if group == 1, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_CR = r(StatTotal)
{txt}  8{com}. 
. tabstat DT_score_pre_std rosen_pre_std cpcs_pre_std female grade_2 if group == 2, stat(`j') save
{txt}  9{com}. matrix list r(StatTotal)
{txt} 10{com}. matrix `j'_TA = r(StatTotal)
{txt} 11{com}. 
. tabstat DT_score_pre_std rosen_pre_std cpcs_pre_std female grade_2 if group == 3, stat(`j') save
{txt} 12{com}. matrix list r(StatTotal)
{txt} 13{com}. matrix `j'_CA = r(StatTotal)
{txt} 14{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d    female   grade_2
{hline 9}{c +}{hline 50}
{ralign 8:Mean} {...}
{c |}{...}
 {res}  .131626  .0147665  .1086469  .6068966  .6137931
{txt}{hline 9}{c BT}{hline 50}
{res}
{txt}r(StatTotal)[1,5]
      DT_score_p~d  rosen_pre_~d  cpcs_pre_std        female       grade_2
Mean {res}    .13162601     .01476645     .10864695     .60689655      .6137931
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d    female   grade_2
{hline 9}{c +}{hline 50}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .2065588 -.0808116  -.240825  .6122449  .6632653
{txt}{hline 9}{c BT}{hline 50}
{res}
{txt}r(StatTotal)[1,5]
      DT_score_p~d  rosen_pre_~d  cpcs_pre_std        female       grade_2
Mean {res}    .20655877    -.08081157    -.24082502      .6122449     .66326531
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d    female   grade_2
{hline 9}{c +}{hline 50}
{ralign 8:Mean} {...}
{c |}{...}
 {res}-.0571177 -.0219281  .0621693  .5628272   .591623
{txt}{hline 9}{c BT}{hline 50}
{res}
{txt}r(StatTotal)[1,5]
      DT_score_p~d  rosen_pre_~d  cpcs_pre_std        female       grade_2
Mean {res}   -.05711768    -.02192813      .0621693     .56282723     .59162304
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d    female   grade_2
{hline 9}{c +}{hline 50}
{ralign 8:Mean} {...}
{c |}{...}
 {res}-.0484936    .03725 -.0418459  .5894737  .5342105
{txt}{hline 9}{c BT}{hline 50}
{res}
{txt}r(StatTotal)[1,5]
      DT_score_p~d  rosen_pre_~d  cpcs_pre_std        female       grade_2
Mean {res}   -.04849356     .03725002    -.04184593     .58947368     .53421053
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d    female   grade_2
{hline 9}{c +}{hline 50}
{ralign 8:SD} {...}
{c |}{...}
 {res} .9720702  .9813127  .9714465  .4901325  .4885666
{txt}{hline 9}{c BT}{hline 50}
{res}
{txt}r(StatTotal)[1,5]
    DT_score_p~d  rosen_pre_~d  cpcs_pre_std        female       grade_2
SD {res}    .97207023     .98131273     .97144648     .49013252      .4885666
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d    female   grade_2
{hline 9}{c +}{hline 50}
{ralign 8:SD} {...}
{c |}{...}
 {res} .9189083  1.045447  1.124362  .4897433  .4750231
{txt}{hline 9}{c BT}{hline 50}
{res}
{txt}r(StatTotal)[1,5]
    DT_score_p~d  rosen_pre_~d  cpcs_pre_std        female       grade_2
SD {res}    .91890826     1.0454466     1.1243618     .48974332     .47502312
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d    female   grade_2
{hline 9}{c +}{hline 50}
{ralign 8:SD} {...}
{c |}{...}
 {res} 1.010309  .9292978  .9123191  .4966876  .4921782
{txt}{hline 9}{c BT}{hline 50}
{res}
{txt}r(StatTotal)[1,5]
    DT_score_p~d  rosen_pre_~d  cpcs_pre_std        female       grade_2
SD {res}     1.010309     .92929779     .91231914     .49668758     .49217817
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d    female   grade_2
{hline 9}{c +}{hline 50}
{ralign 8:SD} {...}
{c |}{...}
 {res} 1.012869  1.071214  1.058732  .4925779  .4994859
{txt}{hline 9}{c BT}{hline 50}
{res}
{txt}r(StatTotal)[1,5]
    DT_score_p~d  rosen_pre_~d  cpcs_pre_std        female       grade_2
SD {res}    1.0128693     1.0712145      1.058732     .49257788     .49948592
{reset}
{com}. 
. /// Internal Validity for the Respondents
> foreach outcome in DT_score_pre_std rosen_pre_std cpcs_pre_std female grade_2 {c -(}
{txt}  2{com}. regress `outcome' i.group
{txt}  3{com}. test (0.group = 1.group) (2.group = 3.group)
{txt}  4{com}. scalar IValR_p_`outcome' = r(p)
{txt}  5{com}. {c )-}

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       968
{txt}{hline 13}{c +}{hline 34}   F(3, 964)       = {res}     2.88
{txt}       Model {c |} {res}  8.6031543         3   2.8677181   {txt}Prob > F        ={res}    0.0348
{txt}    Residual {c |} {res}  958.39683       964  .994187583   {txt}R-squared       ={res}    0.0089
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0058
{txt}       Total {c |} {res} 966.999985       967  .999999984   {txt}Root MSE        =   {res} .99709

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}DT_score_p~d{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}group {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .0749328{col 26}{space 2} .1317923{col 37}{space 1}    0.57{col 46}{space 3}0.570{col 54}{space 4}-.1837001{col 67}{space 3} .3335657
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.1887437{col 26}{space 2} .0977872{col 37}{space 1}   -1.93{col 46}{space 3}0.054{col 54}{space 4} -.380644{col 67}{space 3} .0031566
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.1801196{col 26}{space 2}  .098512{col 37}{space 1}   -1.83{col 46}{space 3}0.068{col 54}{space 4}-.3734422{col 67}{space 3} .0132031
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}  .131626{col 26}{space 2} .0830908{col 37}{space 1}    1.58{col 46}{space 3}0.113{col 54}{space 4}-.0314337{col 67}{space 3} .2946857
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{p 0 7}{space 1}{text:( 1)}{space 1} 0b.group - 1.group = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} 2.group - 3.group = 0{p_end}

{txt}       F(  2,   964) ={res}    0.17
{txt}{col 13}Prob > F ={res}    0.8450

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,005
{txt}{hline 13}{c +}{hline 34}   F(3, 1001)      = {res}     0.46
{txt}       Model {c |} {res} 1.38256321         3  .460854404   {txt}Prob > F        ={res}    0.7121
{txt}    Residual {c |} {res} 1008.61746     1,001  1.00760985   {txt}R-squared       ={res}    0.0014
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}   -0.0016
{txt}       Total {c |} {res} 1010.00003     1,004  1.00597612   {txt}Root MSE        =   {res} 1.0038

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}rosen_pre_~d{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}group {c |}
{space 10}1  {c |}{col 14}{res}{space 2} -.095578{col 26}{space 2}  .131266{col 37}{space 1}   -0.73{col 46}{space 3}0.467{col 54}{space 4}-.3531661{col 67}{space 3} .1620101
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.0366946{col 26}{space 2}  .097912{col 37}{space 1}   -0.37{col 46}{space 3}0.708{col 54}{space 4}-.2288308{col 67}{space 3} .1554417
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .0224836{col 26}{space 2} .0979828{col 37}{space 1}    0.23{col 46}{space 3}0.819{col 54}{space 4}-.1697918{col 67}{space 3} .2147589
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} .0147665{col 26}{space 2} .0833609{col 37}{space 1}    0.18{col 46}{space 3}0.859{col 54}{space 4}-.1488156{col 67}{space 3} .1783485
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{p 0 7}{space 1}{text:( 1)}{space 1} 0b.group - 1.group = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} 2.group - 3.group = 0{p_end}

{txt}       F(  2,  1001) ={res}    0.60
{txt}{col 13}Prob > F ={res}    0.5511

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,005
{txt}{hline 13}{c +}{hline 34}   F(3, 1001)      = {res}     3.18
{txt}       Model {c |} {res} 9.53712778         3  3.17904259   {txt}Prob > F        ={res}    0.0233
{txt}    Residual {c |} {res} 1000.46288     1,001  .999463416   {txt}R-squared       ={res}    0.0094
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0065
{txt}       Total {c |} {res} 1010.00001     1,004   1.0059761   {txt}Root MSE        =   {res} .99973

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}cpcs_pre_std{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}group {c |}
{space 10}1  {c |}{col 14}{res}{space 2} -.349472{col 26}{space 2} .1307343{col 37}{space 1}   -2.67{col 46}{space 3}0.008{col 54}{space 4}-.6060167{col 67}{space 3}-.0929272
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.0464776{col 26}{space 2} .0975154{col 37}{space 1}   -0.48{col 46}{space 3}0.634{col 54}{space 4}-.2378356{col 67}{space 3} .1448803
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.1504929{col 26}{space 2}  .097586{col 37}{space 1}   -1.54{col 46}{space 3}0.123{col 54}{space 4}-.3419894{col 67}{space 3} .0410036
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} .1086469{col 26}{space 2} .0830232{col 37}{space 1}    1.31{col 46}{space 3}0.191{col 54}{space 4}-.0542725{col 67}{space 3} .2715664
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{p 0 7}{space 1}{text:( 1)}{space 1} 0b.group - 1.group = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} 2.group - 3.group = 0{p_end}

{txt}       F(  2,  1001) ={res}    4.60
{txt}{col 13}Prob > F ={res}    0.0102

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,005
{txt}{hline 13}{c +}{hline 34}   F(3, 1001)      = {res}     0.46
{txt}       Model {c |} {res} .336822727         3  .112274242   {txt}Prob > F        ={res}    0.7096
{txt}    Residual {c |} {res} 243.808451     1,001  .243564886   {txt}R-squared       ={res}    0.0014
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}   -0.0016
{txt}       Total {c |} {res} 244.145274     1,004  .243172583   {txt}Root MSE        =   {res} .49352

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      female{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}group {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .0053483{col 26}{space 2} .0645377{col 37}{space 1}    0.08{col 46}{space 3}0.934{col 54}{space 4}-.1212963{col 67}{space 3}  .131993
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.0440693{col 26}{space 2}  .048139{col 37}{space 1}   -0.92{col 46}{space 3}0.360{col 54}{space 4}-.1385342{col 67}{space 3} .0503956
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.0174229{col 26}{space 2} .0481738{col 37}{space 1}   -0.36{col 46}{space 3}0.718{col 54}{space 4}-.1119561{col 67}{space 3} .0771104
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} .6068966{col 26}{space 2} .0409848{col 37}{space 1}   14.81{col 46}{space 3}0.000{col 54}{space 4} .5264705{col 67}{space 3} .6873226
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{p 0 7}{space 1}{text:( 1)}{space 1} 0b.group - 1.group = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} 2.group - 3.group = 0{p_end}

{txt}       F(  2,  1001) ={res}    0.28
{txt}{col 13}Prob > F ={res}    0.7550

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,005
{txt}{hline 13}{c +}{hline 34}   F(3, 1001)      = {res}     2.32
{txt}       Model {c |} {res} 1.69336428         3   .56445476   {txt}Prob > F        ={res}    0.0735
{txt}    Residual {c |} {res} 243.108626     1,001   .24286576   {txt}R-squared       ={res}    0.0069
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0039
{txt}       Total {c |} {res}  244.80199     1,004  .243826683   {txt}Root MSE        =   {res} .49281

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     grade_2{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}group {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .0494722{col 26}{space 2}  .064445{col 37}{space 1}    0.77{col 46}{space 3}0.443{col 54}{space 4}-.0769906{col 67}{space 3}  .175935
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.0221701{col 26}{space 2} .0480699{col 37}{space 1}   -0.46{col 46}{space 3}0.645{col 54}{space 4}-.1164993{col 67}{space 3} .0721592
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.0795826{col 26}{space 2} .0481046{col 37}{space 1}   -1.65{col 46}{space 3}0.098{col 54}{space 4}-.1739801{col 67}{space 3} .0148149
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} .6137931{col 26}{space 2}  .040926{col 37}{space 1}   15.00{col 46}{space 3}0.000{col 54}{space 4} .5334825{col 67}{space 3} .6941037
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{p 0 7}{space 1}{text:( 1)}{space 1} 0b.group - 1.group = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} 2.group - 3.group = 0{p_end}

{txt}       F(  2,  1001) ={res}    1.59
{txt}{col 13}Prob > F ={res}    0.2050
{txt}
{com}. 
. /// Internal Validity for the Population
> 
. foreach outcome in DT_score_pre_std rosen_pre_std cpcs_pre_std female grade_2 {c -(}
{txt}  2{com}. regress `outcome' i.group
{txt}  3{com}. test (0.group = 1.group) (1.group = 2.group) (2.group = 3.group)
{txt}  4{com}. scalar IValP_p_`outcome' = r(p)
{txt}  5{com}. {c )-}

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       968
{txt}{hline 13}{c +}{hline 34}   F(3, 964)       = {res}     2.88
{txt}       Model {c |} {res}  8.6031543         3   2.8677181   {txt}Prob > F        ={res}    0.0348
{txt}    Residual {c |} {res}  958.39683       964  .994187583   {txt}R-squared       ={res}    0.0089
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0058
{txt}       Total {c |} {res} 966.999985       967  .999999984   {txt}Root MSE        =   {res} .99709

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}DT_score_p~d{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}group {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .0749328{col 26}{space 2} .1317923{col 37}{space 1}    0.57{col 46}{space 3}0.570{col 54}{space 4}-.1837001{col 67}{space 3} .3335657
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.1887437{col 26}{space 2} .0977872{col 37}{space 1}   -1.93{col 46}{space 3}0.054{col 54}{space 4} -.380644{col 67}{space 3} .0031566
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.1801196{col 26}{space 2}  .098512{col 37}{space 1}   -1.83{col 46}{space 3}0.068{col 54}{space 4}-.3734422{col 67}{space 3} .0132031
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}  .131626{col 26}{space 2} .0830908{col 37}{space 1}    1.58{col 46}{space 3}0.113{col 54}{space 4}-.0314337{col 67}{space 3} .2946857
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{p 0 7}{space 1}{text:( 1)}{space 1} 0b.group - 1.group = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} 1.group - 2.group = 0{p_end}
{p 0 7}{space 1}{text:( 3)}{space 1} 2.group - 3.group = 0{p_end}

{txt}       F(  3,   964) ={res}    2.88
{txt}{col 13}Prob > F ={res}    0.0348

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,005
{txt}{hline 13}{c +}{hline 34}   F(3, 1001)      = {res}     0.46
{txt}       Model {c |} {res} 1.38256321         3  .460854404   {txt}Prob > F        ={res}    0.7121
{txt}    Residual {c |} {res} 1008.61746     1,001  1.00760985   {txt}R-squared       ={res}    0.0014
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}   -0.0016
{txt}       Total {c |} {res} 1010.00003     1,004  1.00597612   {txt}Root MSE        =   {res} 1.0038

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}rosen_pre_~d{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}group {c |}
{space 10}1  {c |}{col 14}{res}{space 2} -.095578{col 26}{space 2}  .131266{col 37}{space 1}   -0.73{col 46}{space 3}0.467{col 54}{space 4}-.3531661{col 67}{space 3} .1620101
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.0366946{col 26}{space 2}  .097912{col 37}{space 1}   -0.37{col 46}{space 3}0.708{col 54}{space 4}-.2288308{col 67}{space 3} .1554417
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .0224836{col 26}{space 2} .0979828{col 37}{space 1}    0.23{col 46}{space 3}0.819{col 54}{space 4}-.1697918{col 67}{space 3} .2147589
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} .0147665{col 26}{space 2} .0833609{col 37}{space 1}    0.18{col 46}{space 3}0.859{col 54}{space 4}-.1488156{col 67}{space 3} .1783485
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{p 0 7}{space 1}{text:( 1)}{space 1} 0b.group - 1.group = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} 1.group - 2.group = 0{p_end}
{p 0 7}{space 1}{text:( 3)}{space 1} 2.group - 3.group = 0{p_end}

{txt}       F(  3,  1001) ={res}    0.46
{txt}{col 13}Prob > F ={res}    0.7121

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,005
{txt}{hline 13}{c +}{hline 34}   F(3, 1001)      = {res}     3.18
{txt}       Model {c |} {res} 9.53712778         3  3.17904259   {txt}Prob > F        ={res}    0.0233
{txt}    Residual {c |} {res} 1000.46288     1,001  .999463416   {txt}R-squared       ={res}    0.0094
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0065
{txt}       Total {c |} {res} 1010.00001     1,004   1.0059761   {txt}Root MSE        =   {res} .99973

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}cpcs_pre_std{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}group {c |}
{space 10}1  {c |}{col 14}{res}{space 2} -.349472{col 26}{space 2} .1307343{col 37}{space 1}   -2.67{col 46}{space 3}0.008{col 54}{space 4}-.6060167{col 67}{space 3}-.0929272
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.0464776{col 26}{space 2} .0975154{col 37}{space 1}   -0.48{col 46}{space 3}0.634{col 54}{space 4}-.2378356{col 67}{space 3} .1448803
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.1504929{col 26}{space 2}  .097586{col 37}{space 1}   -1.54{col 46}{space 3}0.123{col 54}{space 4}-.3419894{col 67}{space 3} .0410036
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} .1086469{col 26}{space 2} .0830232{col 37}{space 1}    1.31{col 46}{space 3}0.191{col 54}{space 4}-.0542725{col 67}{space 3} .2715664
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{p 0 7}{space 1}{text:( 1)}{space 1} 0b.group - 1.group = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} 1.group - 2.group = 0{p_end}
{p 0 7}{space 1}{text:( 3)}{space 1} 2.group - 3.group = 0{p_end}

{txt}       F(  3,  1001) ={res}    3.18
{txt}{col 13}Prob > F ={res}    0.0233

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,005
{txt}{hline 13}{c +}{hline 34}   F(3, 1001)      = {res}     0.46
{txt}       Model {c |} {res} .336822727         3  .112274242   {txt}Prob > F        ={res}    0.7096
{txt}    Residual {c |} {res} 243.808451     1,001  .243564886   {txt}R-squared       ={res}    0.0014
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}   -0.0016
{txt}       Total {c |} {res} 244.145274     1,004  .243172583   {txt}Root MSE        =   {res} .49352

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      female{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}group {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .0053483{col 26}{space 2} .0645377{col 37}{space 1}    0.08{col 46}{space 3}0.934{col 54}{space 4}-.1212963{col 67}{space 3}  .131993
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.0440693{col 26}{space 2}  .048139{col 37}{space 1}   -0.92{col 46}{space 3}0.360{col 54}{space 4}-.1385342{col 67}{space 3} .0503956
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.0174229{col 26}{space 2} .0481738{col 37}{space 1}   -0.36{col 46}{space 3}0.718{col 54}{space 4}-.1119561{col 67}{space 3} .0771104
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} .6068966{col 26}{space 2} .0409848{col 37}{space 1}   14.81{col 46}{space 3}0.000{col 54}{space 4} .5264705{col 67}{space 3} .6873226
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{p 0 7}{space 1}{text:( 1)}{space 1} 0b.group - 1.group = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} 1.group - 2.group = 0{p_end}
{p 0 7}{space 1}{text:( 3)}{space 1} 2.group - 3.group = 0{p_end}

{txt}       F(  3,  1001) ={res}    0.46
{txt}{col 13}Prob > F ={res}    0.7096

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,005
{txt}{hline 13}{c +}{hline 34}   F(3, 1001)      = {res}     2.32
{txt}       Model {c |} {res} 1.69336428         3   .56445476   {txt}Prob > F        ={res}    0.0735
{txt}    Residual {c |} {res} 243.108626     1,001   .24286576   {txt}R-squared       ={res}    0.0069
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0039
{txt}       Total {c |} {res}  244.80199     1,004  .243826683   {txt}Root MSE        =   {res} .49281

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     grade_2{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}group {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .0494722{col 26}{space 2}  .064445{col 37}{space 1}    0.77{col 46}{space 3}0.443{col 54}{space 4}-.0769906{col 67}{space 3}  .175935
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.0221701{col 26}{space 2} .0480699{col 37}{space 1}   -0.46{col 46}{space 3}0.645{col 54}{space 4}-.1164993{col 67}{space 3} .0721592
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.0795826{col 26}{space 2} .0481046{col 37}{space 1}   -1.65{col 46}{space 3}0.098{col 54}{space 4}-.1739801{col 67}{space 3} .0148149
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} .6137931{col 26}{space 2}  .040926{col 37}{space 1}   15.00{col 46}{space 3}0.000{col 54}{space 4} .5334825{col 67}{space 3} .6941037
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{p 0 7}{space 1}{text:( 1)}{space 1} 0b.group - 1.group = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} 1.group - 2.group = 0{p_end}
{p 0 7}{space 1}{text:( 3)}{space 1} 2.group - 3.group = 0{p_end}

{txt}       F(  3,  1001) ={res}    2.32
{txt}{col 13}Prob > F ={res}    0.0735
{txt}
{com}. 
. /// Make Table
> tempname hh2
{txt}
{com}. file open `hh2' using "$path_output/ghanem_attrition_test.tex", write replace
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "% Author: Kazuma Takakura" _newline
{txt}
{com}. file write `hh2' "% Date: `c(current_date)'" _newline
{txt}
{com}. file write `hh2' "% Time: `c(current_time)'" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. 
. file write `hh2' "\begin{c -(}table{c )-}[h!]\footnotesize" _newline
{txt}
{com}. file write `hh2' "  \centering" _newline
{txt}
{com}. file write `hh2' "  \caption{c -(}Internal Validity in the Presence of Attrition{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:ghanem_attrition{c )-}" _newline
{txt}
{com}. file write `hh2' "\scalebox{c -(}1{c )-}{c -(}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}threeparttable{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "\begin{c -(}tabular{c )-}{c -(}lcccccc{c )-}\toprule" _newline
{txt}
{com}. 
. file write `hh2' "   & \multicolumn{c -(}4{c )-}{c -(}c{c )-}{c -(}Mean Baseline Outcome by Group{c )-} & \multicolumn{c -(}2{c )-}{c -(}c{c )-}{c -(}p-value{c )-} \\ " _newline
{txt}
{com}. file write `hh2' " \cmidrule(lr){c -(}2-5{c )-} \cmidrule(lr){c -(}6-7{c )-} "
{txt}
{com}. file write `hh2' " Dependent Variable & TR & CR & TA & CA & IVal-R & IVal-P   \\\midrule\midrule" _newline
{txt}
{com}. file write `hh2' " DT score^{c -(}a{c )-} & " %04.3f (mean_TR[1,1]) " & " %04.3f (mean_CR[1,1]) " & " %04.3f (mean_TA[1,1]) " & " %04.3f (mean_CA[1,1]) " & " %04.3f (IValR_p_DT_score_pre_std) " & " %04.3f (IValP_p_DT_score_pre_std) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_TR[1,1]) " ] & [ " %04.3f (sd_CR[1,1]) " ] & [ " %04.3f (sd_TA[1,1]) " ] &  [ " %04.3f (sd_CA[1,1]) " ] & &    \\ " _newline
{txt}
{com}. 
. file write `hh2' " RSES^{c -(}a{c )-} & " %04.3f (mean_TR[1,2]) " & " %04.3f (mean_CR[1,2]) " & " %04.3f (mean_TA[1,2]) " & " %04.3f (mean_CA[1,2]) " & " %04.3f (IValR_p_rosen_pre_std) " & " %04.3f (IValP_p_rosen_pre_std) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_TR[1,2]) " ] & [ " %04.3f (sd_CR[1,2]) " ] & [ " %04.3f (sd_TA[1,2]) " ] &  [ " %04.3f (sd_CA[1,2]) " ] & &    \\ " _newline
{txt}
{com}. 
. file write `hh2' " CPCS^{c -(}a{c )-} & " %04.3f (mean_TR[1,3]) " & " %04.3f (mean_CR[1,3]) " & " %04.3f (mean_TA[1,3]) " & " %04.3f (mean_CA[1,3]) " & " %04.3f (IValR_p_cpcs_pre_std) " & " %04.3f (IValP_p_cpcs_pre_std) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_TR[1,3]) " ] & [ " %04.3f (sd_CR[1,3]) " ] & [ " %04.3f (sd_TA[1,3]) " ] &  [ " %04.3f (sd_CA[1,3]) " ] & &    \\ " _newline
{txt}
{com}. 
. file write `hh2' " Female & " %04.3f (mean_TR[1,4]) " & " %04.3f (mean_CR[1,4]) " & " %04.3f (mean_TA[1,4]) " & " %04.3f (mean_CA[1,4]) " & " %04.3f (IValR_p_female) " & " %04.3f (IValP_p_female) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_TR[1,4]) " ] & [ " %04.3f (sd_CR[1,4]) " ] & [ " %04.3f (sd_TA[1,4]) " ] &  [ " %04.3f (sd_CA[1,4]) " ] & &    \\ " _newline
{txt}
{com}. 
. file write `hh2' " Grade 3 & " %04.3f (mean_TR[1,5]) " & " %04.3f (mean_CR[1,5]) " & " %04.3f (mean_TA[1,5]) " & " %04.3f (mean_CA[1,5]) " & " %04.3f (IValR_p_grade_2) " & " %04.3f (IValP_p_grade_2) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_TR[1,5]) " ] & [ " %04.3f (sd_CR[1,5]) " ] & [ " %04.3f (sd_TA[1,5]) " ] &  [ " %04.3f (sd_CA[1,5]) " ] & &    \\ " _newline
{txt}
{com}. 
. file write `hh2' " \\ "_newline
{txt}
{com}. 
. 
. 
. file write `hh2' "\midrule" _newline
{txt}
{com}. file write `hh2' "\end{c -(}tabular{c )-}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\item (a) Variables are standardized using the average and variance of the whole baseline sample in the March 2016 survey. " _newline
{txt}
{com}. file write `hh2' "\item (b) Standard deviations are reported in square brackets." _newline
{txt}
{com}. file write `hh2' "\item (c) The mean baseline outcomes correspond to the groups of treatment respondents (TR), control respondents (CR), treatment attritors (TA), and control attritors (CA)." _newline
{txt}
{com}. file write `hh2' "\end{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}threeparttable{c )-}" _newline
{txt}
{com}. file write `hh2' "{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:addlabel{c )-}%" _newline
{txt}
{com}. file write `hh2' "\end{c -(}table{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. file close `hh2'
{txt}
{com}. 
{txt}end of do-file

{com}. 
. do "$path_do/2_table_3.do" 
{txt}
{com}. /// do file for create "Table 3. Long-term Effects"
> set seed 123
{txt}
{com}. 
. use "$path_data/temp/followup_student_parents_matched", replace
{txt}
{com}. 
. 
. // variable
. gen gend = q1d - 1
{txt}
{com}. 
. // PSM
. local controls DT_score_pre_std_missing_0 rosen_pre_std_missing_0 cpcs_pre_std_missing_0 i.grade gend branch1 branch2 branch3 income_source1 income_source2 income_source3 income_source4 last_income_per_member hhmember hhheadage hhheadeduyear phone_survey age_tchr 
{txt}
{com}. 
. teffects psmatch (followup_cog_std) (treatment `controls')
{res}
{txt}Treatment-effects estimation{col 48}Number of obs {col 67}= {res}       243
{txt:Estimator}{col 16}:{res: propensity-score matching}{col 48}{txt:Matches: requested }{col 67}{txt:=}          1
{txt:Outcome model}{col 16}:{res: matching}{txt}{col 63}min {col 67}= {res}         1
{txt:Treatment model}{col 16}:{res: logit}{col 63}{txt:max }{col 67}{txt:=}          1
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}   AI robust
{col 1}followup_c~d{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ATE          {txt}{c |}
{space 3}treatment {c |}
{space 3}(1 vs 0)  {c |}{col 14}{res}{space 2}-.2234479{col 26}{space 2} .1415597{col 37}{space 1}   -1.58{col 46}{space 3}0.114{col 54}{space 4}-.5008998{col 67}{space 3} .0540039
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *teffects overlap, nolabel bw(0.02)
. matrix cog_psm = r(table)
{txt}
{com}. scalar cog_psm_n = e(N)
{txt}
{com}. 
. teffects psmatch (RSES_std) (treatment `controls') 
{res}
{txt}Treatment-effects estimation{col 48}Number of obs {col 67}= {res}       236
{txt:Estimator}{col 16}:{res: propensity-score matching}{col 48}{txt:Matches: requested }{col 67}{txt:=}          1
{txt:Outcome model}{col 16}:{res: matching}{txt}{col 63}min {col 67}= {res}         1
{txt:Treatment model}{col 16}:{res: logit}{col 63}{txt:max }{col 67}{txt:=}          1
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}   AI robust
{col 1}    RSES_std{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ATE          {txt}{c |}
{space 3}treatment {c |}
{space 3}(1 vs 0)  {c |}{col 14}{res}{space 2} .5343905{col 26}{space 2} .1532643{col 37}{space 1}    3.49{col 46}{space 3}0.000{col 54}{space 4} .2339979{col 67}{space 3}  .834783
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix rses_psm = r(table)
{txt}
{com}. scalar rses_psm_n = e(N)
{txt}
{com}. 
. teffects psmatch (CPCS_std) (treatment `controls') 
{res}
{txt}Treatment-effects estimation{col 48}Number of obs {col 67}= {res}       236
{txt:Estimator}{col 16}:{res: propensity-score matching}{col 48}{txt:Matches: requested }{col 67}{txt:=}          1
{txt:Outcome model}{col 16}:{res: matching}{txt}{col 63}min {col 67}= {res}         1
{txt:Treatment model}{col 16}:{res: logit}{col 63}{txt:max }{col 67}{txt:=}          1
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}   AI robust
{col 1}    CPCS_std{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ATE          {txt}{c |}
{space 3}treatment {c |}
{space 3}(1 vs 0)  {c |}{col 14}{res}{space 2}  .583262{col 26}{space 2} .1502653{col 37}{space 1}    3.88{col 46}{space 3}0.000{col 54}{space 4} .2887474{col 67}{space 3} .8777766
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix cpcs_psm = r(table)
{txt}
{com}. scalar cpcs_psm_n = e(N)
{txt}
{com}. 
. // IPWRA
. teffects ipwra (followup_cog_std) (treatment `controls'), vce (cluster school_no)

{res}{txt}Iteration 0:{space 2}EE criterion = {res: 6.548e-15}  
Iteration 1:{space 2}EE criterion = {res: 3.633e-26}  
{res}
{txt}Treatment-effects estimation{col 49}Number of obs {col 67}= {res}       243
{txt:Estimator}{col 16}:{res: IPW regression adjustment}
{txt:Outcome model}{col 16}:{res: linear}
{txt:Treatment model}{col 16}:{res: logit}
{txt}{ralign 92:(Std. err. adjusted for {res:33} clusters in {res:school_no})}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}          followup_cog_std{col 28}{c |} Coefficient{col 40}  std. err.{col 52}      z{col 60}   P>|z|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ATE                        {txt}{c |}
{space 17}treatment {c |}
{space 17}(1 vs 0)  {c |}{col 28}{res}{space 2}-.2664636{col 40}{space 2} .1252781{col 51}{space 1}   -2.13{col 60}{space 3}0.033{col 68}{space 4}-.5120041{col 81}{space 3}-.0209231
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}POmean                     {txt}{c |}
{space 17}treatment {c |}
{space 24}0  {c |}{col 28}{res}{space 2} .1267397{col 40}{space 2}  .088203{col 51}{space 1}    1.44{col 60}{space 3}0.151{col 68}{space 4}-.0461349{col 81}{space 3} .2996143
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix cog_ipwra = r(table)
{txt}
{com}. scalar cog_ipwra_n = e(N)
{txt}
{com}. 
. teffects ipwra (RSES_std) (treatment `controls'), vce (cluster school_no)

{res}{txt}Iteration 0:{space 2}EE criterion = {res: 2.992e-16}  
Iteration 1:{space 2}EE criterion = {res: 2.281e-28}  
{res}
{txt}Treatment-effects estimation{col 49}Number of obs {col 67}= {res}       236
{txt:Estimator}{col 16}:{res: IPW regression adjustment}
{txt:Outcome model}{col 16}:{res: linear}
{txt:Treatment model}{col 16}:{res: logit}
{txt}{ralign 92:(Std. err. adjusted for {res:33} clusters in {res:school_no})}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}                  RSES_std{col 28}{c |} Coefficient{col 40}  std. err.{col 52}      z{col 60}   P>|z|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ATE                        {txt}{c |}
{space 17}treatment {c |}
{space 17}(1 vs 0)  {c |}{col 28}{res}{space 2}  .502171{col 40}{space 2} .1747667{col 51}{space 1}    2.87{col 60}{space 3}0.004{col 68}{space 4} .1596345{col 81}{space 3} .8447075
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}POmean                     {txt}{c |}
{space 17}treatment {c |}
{space 24}0  {c |}{col 28}{res}{space 2}-.2251595{col 40}{space 2} .0948001{col 51}{space 1}   -2.38{col 60}{space 3}0.018{col 68}{space 4}-.4109643{col 81}{space 3}-.0393547
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix rses_ipwra = r(table)
{txt}
{com}. scalar rses_ipwra_n = e(N)
{txt}
{com}. 
. teffects ipwra (CPCS_std) (treatment `controls') , vce (cluster school_no)

{res}{txt}Iteration 0:{space 2}EE criterion = {res: 2.992e-16}  
Iteration 1:{space 2}EE criterion = {res: 2.278e-28}  
{res}
{txt}Treatment-effects estimation{col 49}Number of obs {col 67}= {res}       236
{txt:Estimator}{col 16}:{res: IPW regression adjustment}
{txt:Outcome model}{col 16}:{res: linear}
{txt:Treatment model}{col 16}:{res: logit}
{txt}{ralign 92:(Std. err. adjusted for {res:33} clusters in {res:school_no})}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}                  CPCS_std{col 28}{c |} Coefficient{col 40}  std. err.{col 52}      z{col 60}   P>|z|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ATE                        {txt}{c |}
{space 17}treatment {c |}
{space 17}(1 vs 0)  {c |}{col 28}{res}{space 2} .5274935{col 40}{space 2} .1686685{col 51}{space 1}    3.13{col 60}{space 3}0.002{col 68}{space 4} .1969094{col 81}{space 3} .8580776
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}POmean                     {txt}{c |}
{space 17}treatment {c |}
{space 24}0  {c |}{col 28}{res}{space 2}-.2465068{col 40}{space 2} .0920379{col 51}{space 1}   -2.68{col 60}{space 3}0.007{col 68}{space 4}-.4268978{col 81}{space 3}-.0661158
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix cpcs_ipwra = r(table)
{txt}
{com}. scalar cpcs_ipwra_n = e(N)
{txt}
{com}. 
. // AIPW
. teffects aipw (followup_cog_std) (treatment `controls') , vce (cluster school_no)

{res}{txt}Iteration 0:{space 2}EE criterion = {res: 6.548e-15}  
Iteration 1:{space 2}EE criterion = {res: 3.734e-26}  
{res}
{txt}Treatment-effects estimation{col 49}Number of obs {col 67}= {res}       243
{txt:Estimator}{col 16}:{res: augmented IPW}
{txt:Outcome model}{col 16}:{res: linear by ML}
{txt:Treatment model}{col 16}:{res: logit}
{txt}{ralign 92:(Std. err. adjusted for {res:33} clusters in {res:school_no})}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}          followup_cog_std{col 28}{c |} Coefficient{col 40}  std. err.{col 52}      z{col 60}   P>|z|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ATE                        {txt}{c |}
{space 17}treatment {c |}
{space 17}(1 vs 0)  {c |}{col 28}{res}{space 2}-.2655844{col 40}{space 2} .1258958{col 51}{space 1}   -2.11{col 60}{space 3}0.035{col 68}{space 4}-.5123356{col 81}{space 3}-.0188331
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}POmean                     {txt}{c |}
{space 17}treatment {c |}
{space 24}0  {c |}{col 28}{res}{space 2} .1266577{col 40}{space 2} .0887854{col 51}{space 1}    1.43{col 60}{space 3}0.154{col 68}{space 4}-.0473585{col 81}{space 3} .3006738
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix cog_aipw = r(table)
{txt}
{com}. scalar cog_aipw_n = e(N)
{txt}
{com}. 
. teffects aipw (RSES_std) (treatment `controls') , vce (cluster school_no)

{res}{txt}Iteration 0:{space 2}EE criterion = {res: 2.992e-16}  
Iteration 1:{space 2}EE criterion = {res: 2.445e-28}  
{res}
{txt}Treatment-effects estimation{col 49}Number of obs {col 67}= {res}       236
{txt:Estimator}{col 16}:{res: augmented IPW}
{txt:Outcome model}{col 16}:{res: linear by ML}
{txt:Treatment model}{col 16}:{res: logit}
{txt}{ralign 92:(Std. err. adjusted for {res:33} clusters in {res:school_no})}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}                  RSES_std{col 28}{c |} Coefficient{col 40}  std. err.{col 52}      z{col 60}   P>|z|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ATE                        {txt}{c |}
{space 17}treatment {c |}
{space 17}(1 vs 0)  {c |}{col 28}{res}{space 2} .4994279{col 40}{space 2} .1736836{col 51}{space 1}    2.88{col 60}{space 3}0.004{col 68}{space 4} .1590142{col 81}{space 3} .8398415
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}POmean                     {txt}{c |}
{space 17}treatment {c |}
{space 24}0  {c |}{col 28}{res}{space 2}-.2248917{col 40}{space 2} .0951173{col 51}{space 1}   -2.36{col 60}{space 3}0.018{col 68}{space 4}-.4113183{col 81}{space 3}-.0384652
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix rses_aipw = r(table)
{txt}
{com}. scalar rses_aipw_n = e(N)
{txt}
{com}. 
. teffects aipw (CPCS_std) (treatment `controls') , vce (cluster school_no)

{res}{txt}Iteration 0:{space 2}EE criterion = {res: 2.992e-16}  
Iteration 1:{space 2}EE criterion = {res: 2.424e-28}  
{res}
{txt}Treatment-effects estimation{col 49}Number of obs {col 67}= {res}       236
{txt:Estimator}{col 16}:{res: augmented IPW}
{txt:Outcome model}{col 16}:{res: linear by ML}
{txt:Treatment model}{col 16}:{res: logit}
{txt}{ralign 92:(Std. err. adjusted for {res:33} clusters in {res:school_no})}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}                  CPCS_std{col 28}{c |} Coefficient{col 40}  std. err.{col 52}      z{col 60}   P>|z|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ATE                        {txt}{c |}
{space 17}treatment {c |}
{space 17}(1 vs 0)  {c |}{col 28}{res}{space 2} .5249446{col 40}{space 2} .1677322{col 51}{space 1}    3.13{col 60}{space 3}0.002{col 68}{space 4} .1961956{col 81}{space 3} .8536936
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}POmean                     {txt}{c |}
{space 17}treatment {c |}
{space 24}0  {c |}{col 28}{res}{space 2} -.246192{col 40}{space 2} .0926393{col 51}{space 1}   -2.66{col 60}{space 3}0.008{col 68}{space 4}-.4277617{col 81}{space 3}-.0646222
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix cpcs_aipw = r(table)
{txt}
{com}. scalar cpcs_aipw_n = e(N)
{txt}
{com}. 
. 
. // Lee bounds
. 
. use "$path_data/temp/student_unbalance", replace
{txt}
{com}. 
. gen followup_cog = q6a1_correct + q6a2_correct + q6a3a_correct + q6a3b_correct + q6a4_correct + q6a5_correct
{txt}(769 missing values generated)

{com}. 
. /// non-cog
> // positive: 2,3,5,7,10,11,12,17,18,20,21,22,23,25,26,27,28,29,32,33,34,36,37,39
. // positive-cog:1,13,14,19,24,
. // negative: 4,6,8,9,30,31,35,38,40
. 
. local q99 q6c1 q6c2 q6c3 q6c4 q6c5 q6c6 q6c7 q6c8 q6c9 q6c10 q6c11 q6c12 q6c13 q6c14 q6c15 q6c16 q6c17 q6c18 q6c19 q6c20 ///
> q6c21 q6c22 q6c23 q6c24 q6c25 q6c26 q6c27 q6c28 q6c29 q6c30 q6c31 q6c32 q6c33 q6c34 q6c35 q6c36 q6c37 q6c38 q6c39 q6c40 ///
> q8a1a q8a2a q8a3a q8a4a q8a5a
{txt}
{com}. 
. foreach y in `q99'{c -(}
{txt}  2{com}. replace `y'=.  if `y'==99
{txt}  3{com}. {c )-}
{txt}(2 real changes made, 2 to missing)
(2 real changes made, 2 to missing)
(1 real change made, 1 to missing)
(0 real changes made)
(3 real changes made, 3 to missing)
(2 real changes made, 2 to missing)
(2 real changes made, 2 to missing)
(3 real changes made, 3 to missing)
(1 real change made, 1 to missing)
(1 real change made, 1 to missing)
(3 real changes made, 3 to missing)
(2 real changes made, 2 to missing)
(0 real changes made)
(0 real changes made)
(9 real changes made, 9 to missing)
(9 real changes made, 9 to missing)
(2 real changes made, 2 to missing)
(14 real changes made, 14 to missing)
(13 real changes made, 13 to missing)
(7 real changes made, 7 to missing)
(1 real change made, 1 to missing)
(2 real changes made, 2 to missing)
(2 real changes made, 2 to missing)
(9 real changes made, 9 to missing)
(14 real changes made, 14 to missing)
(1 real change made, 1 to missing)
(3 real changes made, 3 to missing)
(1 real change made, 1 to missing)
(10 real changes made, 10 to missing)
(3 real changes made, 3 to missing)
(1 real change made, 1 to missing)
(1 real change made, 1 to missing)
(0 real changes made)
(0 real changes made)
(36 real changes made, 36 to missing)
(20 real changes made, 20 to missing)
(0 real changes made)
(0 real changes made)
(1 real change made, 1 to missing)
(23 real changes made, 23 to missing)
(4 real changes made, 4 to missing)
(2 real changes made, 2 to missing)
(4 real changes made, 4 to missing)
(0 real changes made)
(1 real change made, 1 to missing)

{com}. 
. gen noncog4 = 5 - q6c4
{txt}(769 missing values generated)

{com}. gen noncog6 = 5 - q6c6
{txt}(771 missing values generated)

{com}. gen noncog8 = 5 - q6c8
{txt}(772 missing values generated)

{com}. gen noncog9 = 5 - q6c9
{txt}(770 missing values generated)

{com}. gen noncog30 = 5 - q6c30
{txt}(772 missing values generated)

{com}. gen noncog31 = 5 - q6c31
{txt}(770 missing values generated)

{com}. gen noncog35 = 5 - q6c35
{txt}(805 missing values generated)

{com}. gen noncog38 = 5 - q6c38
{txt}(769 missing values generated)

{com}. gen noncog40 = 5 - q6c40
{txt}(792 missing values generated)

{com}. 
. gen followup_noncog = q6c1+q6c2+q6c3+noncog4+q6c5+noncog6+q6c7+noncog8+noncog9+q6c10+q6c11+q6c12+q6c13+q6c14+q6c17+q6c18+q6c19+q6c20+q6c21+q6c22+q6c23+q6c24+q6c25+q6c26+q6c27+q6c28+q6c29+noncog30+noncog31+q6c32+q6c33+q6c34+noncog35+q6c36+q6c37+noncog38+noncog40+q6c39
{txt}(847 missing values generated)

{com}. gen followup_noncog2 = q6c2+q6c3+noncog4+q6c5+noncog6+q6c7+noncog8+noncog9+q6c10+q6c11+q6c12+q6c17+q6c18+q6c20+q6c21+q6c22+q6c23+q6c25+q6c26+q6c27+q6c28+q6c29+noncog30+noncog31+q6c32+q6c33+q6c34+noncog35+q6c36+q6c37+noncog38+noncog40+q6c39
{txt}(846 missing values generated)

{com}. 
. sum followup_noncog followup_noncog2

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
followup_n~g {c |}{res}        208    64.64904    16.67701         31        111
{txt}followup_n~2 {c |}{res}        209    55.89952    14.31596         25         94
{txt}
{com}. 
. replace followup_noncog = 190 - followup_noncog
{txt}(208 real changes made)

{com}. replace followup_noncog2 = 180 - followup_noncog2
{txt}(208 real changes made)

{com}. 
. gen RSES = 40 - q6c2 - q6c3 - noncog4 - noncog6 - noncog8 - noncog9 - q6c10 - q6c11
{txt}(778 missing values generated)

{com}. gen CPCS = 50 - q6c2 - q6c3 - noncog4 - q6c5 - noncog6 -q6c7 - noncog8 - noncog9 - q6c10 - q6c11
{txt}(778 missing values generated)

{com}. 
. 
. /// Standardization
> egen DT_score_pre_mean = mean(DT_score_pre)
{txt}
{com}. egen DT_score_pre_sd = sd(DT_score_pre)
{txt}
{com}. gen DT_score_pre_std = (DT_score_pre-DT_score_pre_mean)/DT_score_pre_sd
{txt}(87 missing values generated)

{com}. drop DT_score_pre_mean DT_score_pre_sd 
{txt}
{com}. 
. egen cpcs_pre_mean = mean(cpcs_pre)
{txt}
{com}. egen cpcs_pre_sd = sd(cpcs_pre)
{txt}
{com}. gen cpcs_pre_std = (cpcs_pre-cpcs_pre_mean)/cpcs_pre_sd
{txt}(44 missing values generated)

{com}. drop cpcs_pre_mean cpcs_pre_sd 
{txt}
{com}. 
. egen rosen_pre_mean = mean(rosen_pre)
{txt}
{com}. egen rosen_pre_sd = sd(rosen_pre)
{txt}
{com}. gen rosen_pre_std = (rosen_pre-rosen_pre_mean)/rosen_pre_sd
{txt}(44 missing values generated)

{com}. drop rosen_pre_mean rosen_pre_sd 
{txt}
{com}. 
. replace followup_cog = . if attrition == 1
{txt}(43 real changes made, 43 to missing)

{com}. replace followup_noncog = . if attrition == 1
{txt}(29 real changes made, 29 to missing)

{com}. replace CPCS = . if attrition == 1
{txt}(41 real changes made, 41 to missing)

{com}. replace RSES = . if attrition == 1
{txt}(41 real changes made, 41 to missing)

{com}. 
. egen followup_cog_mean = mean(followup_cog)
{txt}
{com}. egen followup_cog_sd = sd(followup_cog)
{txt}
{com}. gen followup_cog_std = (followup_cog-followup_cog_mean)/followup_cog_sd
{txt}(812 missing values generated)

{com}. drop followup_cog_mean followup_cog_sd 
{txt}
{com}. 
. egen followup_noncog_mean = mean(followup_noncog)
{txt}
{com}. egen followup_noncog_sd = sd(followup_noncog)
{txt}
{com}. gen followup_noncog_std = (followup_noncog - followup_noncog_mean)/followup_noncog_sd
{txt}(876 missing values generated)

{com}. drop followup_noncog_mean followup_noncog_sd 
{txt}
{com}. 
. egen CPCS_mean = mean(CPCS)
{txt}
{com}. egen CPCS_sd = sd(CPCS)
{txt}
{com}. gen CPCS_std = (CPCS - CPCS_mean)/CPCS_sd
{txt}(819 missing values generated)

{com}. drop CPCS_mean CPCS_sd 
{txt}
{com}. 
. egen RSES_mean = mean(RSES)
{txt}
{com}. egen RSES_sd = sd(RSES)
{txt}
{com}. gen RSES_std = (RSES-RSES_mean)/RSES_sd
{txt}(819 missing values generated)

{com}. drop RSES_mean RSES_sd 
{txt}
{com}. 
. gen hyper = 1 if q7d2a == 1 & q7d2b == 2
{txt}(1,041 missing values generated)

{com}. replace hyper = 1 if q7d2a == 1 & q7d2b == 3
{txt}(10 real changes made)

{com}. replace hyper = 1 if q7d2a == 2 & q7d2b == 3
{txt}(18 real changes made)

{com}. gen hypernoinfo = 1 if q7d2a == .
{txt}(220 missing values generated)

{com}. recode hyper hypernoinfo (.=0)
{txt}(1,013 changes made to {bf:hyper})
(220 changes made to {bf:hypernoinfo})

{com}. replace hyper = . if hypernoinfo == 1
{txt}(835 real changes made, 835 to missing)

{com}. 
. gen remain = 1 - attrition
{txt}
{com}. 
. leebounds followup_cog_std treatment, select(remain) vce(bootstrap, reps(1000)) 

{txt}.................................................. 50
.................................................. 100
.................................................. 150
.................................................. 200
.................................................. 250
.................................................. 300
.................................................. 350
.................................................. 400
.................................................. 450
.................................................. 500
.................................................. 550
.................................................. 600
.................................................. 650
.................................................. 700
.................................................. 750
.................................................. 800
.................................................. 850
.................................................. 900
.................................................. 950
.................................................. 1000

Lee (2009) treatment effect bounds

Number of obs.                     =   {res}1005
{txt}Number of selected obs.            =   {res}243
{txt}Trimming porportion                =   {res}0.2549

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}   Observed{col 26}   Bootstrap{col 54}         Norm{col 67}al-based
{col 1}followup_c~d{col 14}{c |} coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}treatment    {txt}{c |}
{space 7}lower {c |}{col 14}{res}{space 2}-.6644101{col 26}{space 2} .1971936{col 37}{space 1}   -3.37{col 46}{space 3}0.001{col 54}{space 4}-1.050903{col 67}{space 3}-.2779176
{txt}{space 7}upper {c |}{col 14}{res}{space 2} .2610873{col 26}{space 2} .1539969{col 37}{space 1}    1.70{col 46}{space 3}0.090{col 54}{space 4}-.0407411{col 67}{space 3} .5629157
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. mat cog_lee_bounds = r(table)
{txt}
{com}. scalar cog_lee_bounds_n = e(N)
{txt}
{com}. 
. leebounds RSES_std treatment, select(remain) vce(bootstrap, reps(1000)) 

{txt}.{err}x{txt}................................................ 50
....{err}x{txt}............................................. 100
.................................................. 150
.....{err}x{txt}............................................ 200
............................{err}x{txt}..................... 250
........{err}x{txt}..............................{err}x{txt}.......... 300
.............................{err}x{txt}.................... 350
.................................................. 400
.................................................. 450
.................................................. 500
.................................................. 550
.................................................. 600
.................................................. 650
.................................................. 700
.................................................. 750
.................................{err}x{txt}................ 800
.................................................. 850
.................................................. 900
..............................................{err}x{txt}... 950
.................................................. 1000

Lee (2009) treatment effect bounds

Number of obs.                     =   {res}998
{txt}Number of selected obs.            =   {res}236
{txt}Trimming porportion                =   {res}0.2480

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}   Observed{col 26}   Bootstrap{col 54}         Norm{col 67}al-based
{col 1}    RSES_std{col 14}{c |} coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}treatment    {txt}{c |}
{space 7}lower {c |}{col 14}{res}{space 2}-.0591159{col 26}{space 2} .2043055{col 37}{space 1}   -0.29{col 46}{space 3}0.772{col 54}{space 4}-.4595474{col 67}{space 3} .3413156
{txt}{space 7}upper {c |}{col 14}{res}{space 2} .8103466{col 26}{space 2}  .175936{col 37}{space 1}    4.61{col 46}{space 3}0.000{col 54}{space 4} .4655183{col 67}{space 3} 1.155175
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. mat rses_lee_bounds = r(table)
{txt}
{com}. scalar rses_lee_bounds_n = e(N)
{txt}
{com}. 
. leebounds CPCS_std treatment, select(remain) vce(bootstrap, reps(1000)) 

{txt}.................................................. 50
.................................................. 100
...........................{err}x{txt}...................... 150
.................................................. 200
.................................................. 250
.................................................. 300
.................................................. 350
.................................................. 400
.................................................. 450
.................................................. 500
.................................................. 550
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.................................................. 650
.................................................. 700
.................................................. 750
.................................................. 800
.................................................. 850
.................................................. 900
.................................................. 950
.................................................. 1000

Lee (2009) treatment effect bounds

Number of obs.                     =   {res}998
{txt}Number of selected obs.            =   {res}236
{txt}Trimming porportion                =   {res}0.2480

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}   Observed{col 26}   Bootstrap{col 54}         Norm{col 67}al-based
{col 1}    CPCS_std{col 14}{c |} coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}treatment    {txt}{c |}
{space 7}lower {c |}{col 14}{res}{space 2}-.0077013{col 26}{space 2} .1895465{col 37}{space 1}   -0.04{col 46}{space 3}0.968{col 54}{space 4}-.3792055{col 67}{space 3} .3638029
{txt}{space 7}upper {c |}{col 14}{res}{space 2}  .839948{col 26}{space 2} .1709533{col 37}{space 1}    4.91{col 46}{space 3}0.000{col 54}{space 4} .5048856{col 67}{space 3}  1.17501
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. mat cpcs_lee_bounds = r(table)
{txt}
{com}. scalar cpcs_lee_bounds_n = e(N)
{txt}
{com}. 
. 
. leebounds followup_cog_std treatment, select(remain) tight(grade) vce(bootstrap, reps(1000)) 

{txt}.................................................. 50
.................................................. 100
.................................................. 150
.................................................. 200
.................................................. 250
............................................{err}x{txt}..... 300
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...........................{err}x{txt}...................... 500
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.................................................. 850
.................{err}x{txt}................................ 900
.................................................. 950
.................................................. 1000

Tightened Lee (2009) treatment effect bounds

Number of obs.                     =   {res}1005
{txt}Number of selected obs.            =   {res}243
{txt}Number of cells                    =   {res}2
{txt}Overall trimming porportion        =   {res}0.2549

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}   Observed{col 26}   Bootstrap{col 54}         Norm{col 67}al-based
{col 1}followup_c~d{col 14}{c |} coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}treatment    {txt}{c |}
{space 7}lower {c |}{col 14}{res}{space 2}-.6127419{col 26}{space 2} .2198418{col 37}{space 1}   -2.79{col 46}{space 3}0.005{col 54}{space 4}-1.043624{col 67}{space 3}-.1818598
{txt}{space 7}upper {c |}{col 14}{res}{space 2} .2297789{col 26}{space 2} .1830233{col 37}{space 1}    1.26{col 46}{space 3}0.209{col 54}{space 4}-.1289403{col 67}{space 3}  .588498
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. mat cog_lee_bounds_tight = r(table)
{txt}
{com}. scalar cog_lee_bounds_tight_n = e(N)
{txt}
{com}. 
. leebounds RSES_std treatment, select(remain) tight(grade) vce(bootstrap, reps(1000)) 

{txt}.................................................. 50
.................................................. 100
.................................................. 150
.............{err}x{txt}.................................... 200
.................................................. 250
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.....{err}x{txt}............................................ 450
.............{err}x{txt}.................................... 500
...{err}x{txt}.............................................. 550
.{err}x{txt}................................................ 600
.................................................. 650
.................................................. 700
...................................{err}x{txt}.............. 750
.................................................. 800
.................................................. 850
.................................................. 900
.................................................. 950
.................................................. 1000

Tightened Lee (2009) treatment effect bounds

Number of obs.                     =   {res}998
{txt}Number of selected obs.            =   {res}236
{txt}Number of cells                    =   {res}2
{txt}Overall trimming porportion        =   {res}0.2480

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}   Observed{col 26}   Bootstrap{col 54}         Norm{col 67}al-based
{col 1}    RSES_std{col 14}{c |} coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}treatment    {txt}{c |}
{space 7}lower {c |}{col 14}{res}{space 2} .0520425{col 26}{space 2} .1988992{col 37}{space 1}    0.26{col 46}{space 3}0.794{col 54}{space 4}-.3377928{col 67}{space 3} .4418778
{txt}{space 7}upper {c |}{col 14}{res}{space 2} .7831147{col 26}{space 2}  .205603{col 37}{space 1}    3.81{col 46}{space 3}0.000{col 54}{space 4} .3801403{col 67}{space 3} 1.186089
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. mat rses_lee_bounds_tight = r(table)
{txt}
{com}. scalar rses_lee_bounds_tight_n = e(N)
{txt}
{com}. 
. leebounds CPCS_std treatment, select(remain) tight(grade) vce(bootstrap, reps(1000)) 

{txt}.................................................. 50
.........................................{err}x{txt}........ 100
.................................................. 150
.................................................. 200
.................................................. 250
.................................................. 300
.................................................. 350
........................{err}x{txt}......................... 400
.................................................. 450
.................................................. 500
.................................................. 550
..............{err}x{txt}..{err}x{txt}..............{err}x{txt}................. 600
.................................................. 650
.................................................. 700
..{err}x{txt}............................................... 750
.................................................. 800
.................................................. 850
.................................................. 900
.......{err}x{txt}....................{err}x{txt}..................... 950
.................................................. 1000

Tightened Lee (2009) treatment effect bounds

Number of obs.                     =   {res}998
{txt}Number of selected obs.            =   {res}236
{txt}Number of cells                    =   {res}2
{txt}Overall trimming porportion        =   {res}0.2480

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}   Observed{col 26}   Bootstrap{col 54}         Norm{col 67}al-based
{col 1}    CPCS_std{col 14}{c |} coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}treatment    {txt}{c |}
{space 7}lower {c |}{col 14}{res}{space 2} .0649935{col 26}{space 2} .2007969{col 37}{space 1}    0.32{col 46}{space 3}0.746{col 54}{space 4}-.3285613{col 67}{space 3} .4585482
{txt}{space 7}upper {c |}{col 14}{res}{space 2} .8158031{col 26}{space 2} .1789297{col 37}{space 1}    4.56{col 46}{space 3}0.000{col 54}{space 4} .4651072{col 67}{space 3} 1.166499
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. mat cpcs_lee_bounds_tight = r(table)
{txt}
{com}. scalar cpcs_lee_bounds_tight_n = e(N)
{txt}
{com}. 
. 
. /*
> /// Manski bounds
> manski_ci, outcome(followup_cog_std) treat(treatment) vce(bootstrap, reps(100)) 
> mat cog_manski_bounds_l = e(worst)
> mat cog_manski_bounds_u = e(best)
> 
> manski_ci, outcome(RSES_std) treat(treatment) vce(bootstrap, reps(100)) 
> mat rses_manski_bounds_l = e(worst)
> mat rses_manski_bounds_u = e(best)
> 
> manski_ci, outcome(CPCS_std) treat(treatment) vce(bootstrap, reps(100)) 
> mat cpcs_manski_bounds_l = e(worst)
> mat cpcs_manski_bounds_u = e(best)
> */
. 
. 
. local outcome cog rses cpcs
{txt}
{com}. local method psm ipwra aipw lee_bounds lee_bounds_tight
{txt}
{com}. 
. foreach o in `outcome'{c -(}
{txt}  2{com}. foreach me in `method' {c -(}
{txt}  3{com}.                 forvalues i = 1/2 {c -(}
{txt}  4{com}.                 if `o'_`me'[4, `i']<=0.01 {c -(}
{txt}  5{com}.                         local star_`o'_`me'_`i' %3s "***"
{txt}  6{com}.                 {c )-}
{txt}  7{com}.                 else if (`o'_`me'[4, `i']>0.01) & (`o'_`me'[4, `i']<=0.05) {c -(}
{txt}  8{com}.                         local star_`o'_`me'_`i' %2s "**"
{txt}  9{com}.                 {c )-}
{txt} 10{com}.                 else if (`o'_`me'[4, `i']>0.05) & (`o'_`me'[4, `i']<=0.10) {c -(}
{txt} 11{com}.                         local star_`o'_`me'_`i' %1s "*"
{txt} 12{com}.                 {c )-}
{txt} 13{com}.                 else {c -(}
{txt} 14{com}.                         local star_`o'_`me'_`i'  ""
{txt} 15{com}.                 {c )-}
{txt} 16{com}.         {c )-} 
{txt} 17{com}. {c )-}  
{txt} 18{com}. {c )-} 
{txt}
{com}. 
. /// Table
> tempname hh2
{txt}
{com}. file open `hh2' using "$path_output/cognitive_robust.tex", write replace
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "% Author: Kazuma Takakura" _newline
{txt}
{com}. file write `hh2' "% Date: `c(current_date)'" _newline
{txt}
{com}. file write `hh2' "% Time: `c(current_time)'" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. 
. file write `hh2' "\begin{c -(}table{c )-}[h!]\footnotesize" _newline
{txt}
{com}. file write `hh2' "  \centering" _newline
{txt}
{com}. file write `hh2' "  \caption{c -(}Long-term Effects{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:robust{c )-}" _newline
{txt}
{com}. file write `hh2' "\scalebox{c -(}0.7{c )-}{c -(}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}threeparttable{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "\begin{c -(}tabular{c )-}{c -(}lccccccc{c )-}\toprule" _newline
{txt}
{com}. 
. 
. file write `hh2' "  & PSM^a & IPWRA^a & AIPW^a & Lee Bound & Lee Bound & Lee Bound & Lee Bound  \\" _newline
{txt}
{com}. file write `hh2' "  &  &  &   & (Lower) & (Upper) &  (Lower, Tight)^b & (Upper, Tight)^b \\\midrule\midrule" _newline
{txt}
{com}. 
. file write `hh2' "  Rapid math test score & " %04.3f (cog_psm[1,1]) `star_cog_psm_1' "  & " %04.3f (cog_ipwra[1,1]) `star_cog_ipwra_1' " & " %04.3f (cog_aipw[1,1]) `star_cog_aipw_1' " & " %04.3f (cog_lee_bounds[1,1]) `star_cog_lee_bounds_1' " & " %04.3f (cog_lee_bounds[1,2]) `star_cog_lee_bounds_2' " & " %04.3f (cog_lee_bounds_tight[1,1])  `star_cog_lee_bounds_tight_1' " & " %04.3f (cog_lee_bounds_tight[1,2]) `star_cog_lee_bounds_tight_2' "  \\ " _newline
{txt}
{com}. file write `hh2' "    & ( XXX ) & (" %04.3f (cog_ipwra[2,1]) ") & (" %04.3f (cog_aipw[2,1]) ") & (" %04.3f (cog_lee_bounds[2,1]) ") & (" %04.3f (cog_lee_bounds[2,2]) ") & (" %04.3f (cog_lee_bounds_tight[2,1]) ") & (" %04.3f (cog_lee_bounds_tight[2,2]) ") \\ " _newline
{txt}
{com}. 
. file write `hh2' "  RSES score & " %04.3f (rses_psm[1,1]) `star_rses_psm_1' " & " %04.3f (rses_ipwra[1,1]) `star_rses_ipwra_1' "  & " %04.3f (rses_aipw[1,1]) `star_rses_aipw_1' " & " %04.3f (rses_lee_bounds[1,1]) `star_rses_lee_bounds_1' " & " %04.3f (rses_lee_bounds[1,2]) `star_rses_lee_bounds_2' " & " %04.3f (rses_lee_bounds_tight[1,1]) `star_rses_lee_bounds_tight_1' " & " %04.3f (rses_lee_bounds_tight[1,2]) `star_rses_lee_bounds_tight_2' "  \\ " _newline
{txt}
{com}. file write `hh2' "     & ( XXX ) & (" %04.3f (rses_ipwra[2,1]) ") & (" %04.3f (rses_aipw[2,1]) ") & (" %04.3f (rses_lee_bounds[2,1]) ") & (" %04.3f (rses_lee_bounds[2,2]) ") & (" %04.3f (rses_lee_bounds_tight[2,1]) ") & (" %04.3f (rses_lee_bounds_tight[2,2]) ") \\ " _newline
{txt}
{com}. 
. file write `hh2' "  CPCS score & " %04.3f (cpcs_psm[1,1]) `star_cpcs_psm_1' " & " %04.3f (cpcs_ipwra[1,1]) `star_cpcs_ipwra_1' "  & " %04.3f (cpcs_aipw[1,1]) `star_cpcs_aipw_1' " & " %04.3f (cpcs_lee_bounds[1,1]) `star_cpcs_lee_bounds_1' " & " %04.3f (cpcs_lee_bounds[1,2]) `star_cpcs_lee_bounds_2' " & " %04.3f (cpcs_lee_bounds_tight[1,1]) `star_cpcs_lee_bounds_tight_1' " & " %04.3f (cpcs_lee_bounds_tight[1,2]) `star_cpcs_lee_bounds_tight_2' "  \\ " _newline
{txt}
{com}. file write `hh2' "    & ( XXX ) & (" %04.3f (cpcs_ipwra[2,1]) ") & (" %04.3f (cpcs_aipw[2,1]) ") & (" %04.3f (cpcs_lee_bounds[2,1]) ") & (" %04.3f (cpcs_lee_bounds[2,2]) ") & (" %04.3f (cpcs_lee_bounds_tight[2,1]) ") & (" %04.3f (cpcs_lee_bounds_tight[2,2]) ") \\ " _newline
{txt}
{com}. 
. 
. file write `hh2' "\midrule" _newline
{txt}
{com}. file write `hh2' "\end{c -(}tabular{c )-}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\item (a) For estimating the propensity score function and the outcome model, we use covariates including student's grade, sex, baseline cognitive and baseline non-cognitive score, DT baseline time, branch dummy (location), parents' income source, last income per family member, number of household members, age of household head, education level of household head, teacher's age, teacher's sex, and phone survey dummy." _newline
{txt}
{com}. file write `hh2' "\item (c) Standard errors are reported within parentheses. For propensity score matching estimation, we calculate clustered bootstrap standard errors based on \cite{c -(}otsu2017bootstrap{c )-}. For IPWRA and AIPW, we calculate clustered standard errors. For Lee bounds estimation, we calculate bootstrap standard errors." _newline
{txt}
{com}. file write `hh2' "\item (d) The numbers of observations are as follows:" (cog_psm_n) " for rapid math test score and " (rses_psm_n) " for RSES and CPCS in PSM, IPWRA, and AIPW. "(cog_lee_bounds_n) " for rapid math test score and " (rses_lee_bounds_n) " for RSES and CPCS in Lee bounds. " _newline
{txt}
{com}. file write `hh2' "\item (e) $^*$ Significant at 10\% level; $^{c -(}**{c )-}$ significant at 5\% level; $^{c -(}***{c )-}$ significant at 1\% level. " _newline
{txt}
{com}. file write `hh2' "\end{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}threeparttable{c )-}" _newline
{txt}
{com}. file write `hh2' "{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}table{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. file close `hh2'
{txt}
{com}. 
. 
{txt}end of do-file

{com}. 
. do "$path_do/2_table_3_data_for_matlab.do"
{txt}
{com}. * This is the do file for creating data files to calculate bootstrap standard errors in PSM 
. 
. use "$path_data/temp/followup_student_parents_matched", clear
{txt}
{com}. 
. 
. // Cog
. preserve
{txt}
{com}. keep if treatment == 1
{txt}(98 observations deleted)

{com}. keep followup_cog_std DT_score_pre_std_missing_0 rosen_pre_std_missing_0 cpcs_pre_std_missing_0 grade student_gender branch1 branch2 branch3 income_source1 income_source2 income_source3 income_source4 last_income_per_member hhmember hhheadage hhheadeduyear phone_survey age_tchr
{txt}
{com}. order followup_cog_std
{txt}
{com}. export delimited using "$path_data/matlab/CogTre.csv", novarnames replace
{res}{txt}file {bf:/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/data/matlab/CogTre.csv} saved

{com}. restore
{txt}
{com}. preserve
{txt}
{com}. keep if treatment == 0
{txt}(145 observations deleted)

{com}. keep followup_cog_std DT_score_pre_std_missing_0 rosen_pre_std_missing_0 cpcs_pre_std_missing_0 grade student_gender branch1 branch2 branch3 income_source1 income_source2 income_source3 income_source4 last_income_per_member hhmember hhheadage hhheadeduyear phone_survey age_tchr
{txt}
{com}. order followup_cog_std
{txt}
{com}. export delimited using "$path_data/matlab/CogCon.csv", novarnames replace
{res}{txt}file {bf:/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/data/matlab/CogCon.csv} saved

{com}. restore
{txt}
{com}. 
. // RSES
. preserve
{txt}
{com}. keep if treatment == 1
{txt}(98 observations deleted)

{com}. keep RSES_std DT_score_pre_std_missing_0 rosen_pre_std_missing_0 cpcs_pre_std_missing_0 grade student_gender branch1 branch2 branch3 income_source1 income_source2 income_source3 income_source4 last_income_per_member hhmember hhheadage hhheadeduyear phone_survey age_tchr
{txt}
{com}. order RSES_std
{txt}
{com}. export delimited using "$path_data/matlab/RSESTre.csv", novarnames replace
{res}{txt}file {bf:/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/data/matlab/RSESTre.csv} saved

{com}. restore
{txt}
{com}. preserve
{txt}
{com}. keep if treatment == 0
{txt}(145 observations deleted)

{com}. keep RSES_std DT_score_pre_std_missing_0 rosen_pre_std_missing_0 cpcs_pre_std_missing_0 grade student_gender branch1 branch2 branch3 income_source1 income_source2 income_source3 income_source4 last_income_per_member hhmember hhheadage hhheadeduyear phone_survey age_tchr
{txt}
{com}. order RSES_std
{txt}
{com}. export delimited using "$path_data/matlab/RSESCon.csv", novarnames replace
{res}{txt}file {bf:/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/data/matlab/RSESCon.csv} saved

{com}. restore
{txt}
{com}. 
. // CPCS
. preserve
{txt}
{com}. keep if treatment == 1
{txt}(98 observations deleted)

{com}. keep CPCS_std DT_score_pre_std_missing_0 rosen_pre_std_missing_0 cpcs_pre_std_missing_0 grade student_gender branch1 branch2 branch3 income_source1 income_source2 income_source3 income_source4 last_income_per_member hhmember hhheadage hhheadeduyear phone_survey age_tchr
{txt}
{com}. order CPCS_std
{txt}
{com}. export delimited using "$path_data/matlab/CPCSTre.csv", novarnames replace
{res}{txt}file {bf:/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/data/matlab/CPCSTre.csv} saved

{com}. restore
{txt}
{com}. preserve
{txt}
{com}. keep if treatment == 0
{txt}(145 observations deleted)

{com}. keep CPCS_std DT_score_pre_std_missing_0 rosen_pre_std_missing_0 cpcs_pre_std_missing_0 grade student_gender branch1 branch2 branch3 income_source1 income_source2 income_source3 income_source4 last_income_per_member hhmember hhheadage hhheadeduyear phone_survey age_tchr
{txt}
{com}. order CPCS_std
{txt}
{com}. export delimited using "$path_data/matlab/CPCSCon.csv", novarnames replace
{res}{txt}file {bf:/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/data/matlab/CPCSCon.csv} saved

{com}. restore
{txt}
{com}. 
. // Cluster
. preserve
{txt}
{com}. keep if treatment == 1
{txt}(98 observations deleted)

{com}. keep school_no
{txt}
{com}. export delimited using "$path_data/matlab/ClusTre.csv", novarnames replace
{res}{txt}file {bf:/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/data/matlab/ClusTre.csv} saved

{com}. restore
{txt}
{com}. preserve
{txt}
{com}. keep if treatment == 0
{txt}(145 observations deleted)

{com}. keep school_no
{txt}
{com}. export delimited using "$path_data/matlab/ClusCon.csv", novarnames replace
{res}{txt}file {bf:/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/data/matlab/ClusCon.csv} saved

{com}. restore
{txt}
{com}. 
{txt}end of do-file

{com}. 
. do "$path_do/2_table_4.do"
{txt}
{com}. * This is the do file to create "Table 4. Heterogeneity among Baseline Abilites"
. set seed 123
{txt}
{com}. 
. use "$path_data/temp/followup_student_parents_matched", replace
{txt}
{com}. 
. egen DT_score_pre_std_med = median(DT_score_pre_std)
{txt}
{com}. gen DT_score_pre_std_upper50 = 1 if DT_score_pre_std>DT_score_pre_std_med
{txt}(121 missing values generated)

{com}. recode DT_score_pre_std_upper50 (.=0)
{txt}(121 changes made to {bf:DT_score_pre_std_upper50})

{com}. 
. egen rosen_pre_std_med = median(rosen_pre_std)
{txt}
{com}. gen rosen_pre_std_upper50 = 1 if rosen_pre_std>rosen_pre_std_med
{txt}(128 missing values generated)

{com}. recode rosen_pre_std_upper50 (.=0)
{txt}(128 changes made to {bf:rosen_pre_std_upper50})

{com}. rename rosen_pre_std_upper50 RSES_std_upper50
{res}{txt}
{com}. 
. egen cpcs_pre_std_med = median(cpcs_pre_std)
{txt}
{com}. gen cpcs_pre_std_upper50 = 1 if cpcs_pre_std>cpcs_pre_std_med
{txt}(135 missing values generated)

{com}. recode cpcs_pre_std_upper50 (.=0)
{txt}(135 changes made to {bf:cpcs_pre_std_upper50})

{com}. rename cpcs_pre_std_upper50 CPCS_std_upper50
{res}{txt}
{com}. 
. 
. 
. /// Cognitive
> wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}122
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 28
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}4.4
{col 69}{txt}max{col 72} = {res} 11
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2944524{col 38}{space 1}  -1.31{col 46}{space 3}0.218{col 54}{space 3}-.7968218{col 66}{space 3} .2159552
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u = e(N)
{txt}
{com}. scalar n_clust_cog_u = e(N_clust)
{txt}
{com}. matrix r2_followup_cog_std_temp = r(table)
{txt}
{com}. 
. 
. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix r2_followup_cog_std_upper_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix r2_followup_cog_std_upper_mean[1,`j'] = r2_followup_cog_std_temp[1,`j']
{txt}  3{com}. * standard error
. * matrix r2_followup_cog_std_upper_se[1,`j'] = r2_followup_cog_std_temp[2,`j']
. * p value
. matrix r2_followup_cog_std_upper_pv[1,`j'] = r2_followup_cog_std_temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}121
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 30
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}4.0
{col 69}{txt}max{col 72} = {res} 10
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} -.156118{col 38}{space 1}  -0.67{col 46}{space 3}0.558{col 54}{space 3}-.6174928{col 66}{space 3} .4100967
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l = e(N)
{txt}
{com}. scalar n_clust_cog_l = e(N_clust)
{txt}
{com}. matrix r2_followup_cog_std_temp = r(table)
{txt}
{com}. 
. 
. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix r2_followup_cog_std_lower_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix r2_followup_cog_std_lower_mean[1,`j'] = r2_followup_cog_std_temp[1,`j']
{txt}  3{com}. * standard error
. * matrix r2_followup_cog_std_lower_se[1,`j'] = r2_followup_cog_std_temp[2,`j']
. * p value
. matrix r2_followup_cog_std_lower_pv[1,`j'] = r2_followup_cog_std_temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std i.treatment##i.DT_score_pre_std_upper50, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:1.treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:1.treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:1.DT_score_pre_std_upper50 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:1.DT_score_pre_std_upper50}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:1.treatment#1.DT_score_pre_std_upper50 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:1.treatment#1.DT_score_pre_std_upper50}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraints             {col 26}{c |}
{res}{col 1}{text}         1.treatment = 0{col 26}{c |}{result}{space 2} -.156118{col 38}{space 1}  -0.67{col 46}{space 3}0.536{col 54}{space 3}-.6346894{col 66}{space 3} .4253064
{col 1}{text}1.DT_score_pre_std_upper{col 26}{c |}
{res}{col 1}{text}                  50 = 0{col 26}{c |}{result}{space 2} .1420924{col 38}{space 1}   0.67{col 46}{space 3}0.512{col 54}{space 3} -.380663{col 66}{space 3} .6680267
{col 26}{text}{c |}
{res}{col 1}{text}            1.treatment#{col 26}{c |}
{res}{col 1}{text}1.DT_score_pre_std_upper{col 26}{c |}
{res}{col 1}{text}                  50 = 0{col 26}{c |}{result}{space 2}-.1383344{col 38}{space 1}  -0.45{col 46}{space 3}0.680{col 54}{space 3}-.8103858{col 66}{space 3} .4858404
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix cog_difference = r(table)
{txt}
{com}. 
. 
.     
. /// Non cognitive
> 
. 
. foreach dep in RSES_std CPCS_std{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment if `dep'_upper50 == 1, cluster(school_no) reps(1000)
{txt}  3{com}.         scalar n_`dep'_u = e(N)
{txt}  4{com}.         scalar n_clust_`dep'_u = e(N_clust)
{txt}  5{com}.         matrix r2_`dep'_temp = r(table)
{txt}  6{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  7{com}.                 matrix r2_`dep'_upper_`s' = J(1,2,.)
{txt}  8{com}.         {c )-}
{txt}  9{com}. 
.         foreach j in 1 2 {c -(}
{txt} 10{com}.         * beta
.         matrix r2_`dep'_upper_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt} 11{com}.         * standard error
.         * matrix r2_`dep'_upper_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_upper_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 12{com}.         {c )-}
{txt} 13{com}. 
. 
.         wildbootstrap reg `dep' treatment if `dep'_upper50 == 0, cluster(school_no) reps(1000)
{txt} 14{com}.         scalar n_`dep'_l = e(N)
{txt} 15{com}.         scalar n_clust_`dep'_l = e(N_clust)
{txt} 16{com}.         matrix r2_`dep'_temp = r(table)
{txt} 17{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt} 18{com}.                 matrix r2_`dep'_lower_`s' = J(1,2,.)
{txt} 19{com}.         {c )-}
{txt} 20{com}. 
.         foreach j in 1 2 {c -(}
{txt} 21{com}.         * beta
.         matrix r2_`dep'_lower_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt} 22{com}.         * standard error
.         * matrix r2_`dep'_lower_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_lower_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 23{com}.         {c )-}
{txt} 24{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}112
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 28
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}4.0
{col 69}{txt}max{col 72} = {res} 11
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .7750596{col 38}{space 1}   3.24{col 46}{space 3}0.008{col 54}{space 3} .2893885{col 66}{space 3} 1.282941
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}124
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 29
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}4.3
{col 69}{txt}max{col 72} = {res} 10
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0948848{col 38}{space 1}   0.43{col 46}{space 3}0.674{col 54}{space 3} -.373688{col 66}{space 3} .5897037
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:19})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}105
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 30
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}3.5
{col 69}{txt}max{col 72} = {res} 10
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .6274947{col 38}{space 1}   2.84{col 46}{space 3}0.014{col 54}{space 3} .1867966{col 66}{space 3} 1.064869
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}131
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 28
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  2
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}4.7
{col 69}{txt}max{col 72} = {res} 10
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3378141{col 38}{space 1}   1.31{col 46}{space 3}0.182{col 54}{space 3}-.1932436{col 66}{space 3}  .895076
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. wildbootstrap reg RSES_std i.treatment##i.RSES_std_upper50, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:1.treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:1.treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:1.RSES_std_upper50 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:1.RSES_std_upper50}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:1.treatment#1.RSES_std_upper50 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:1.treatment#1.RSES_std_upper50}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}236
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.2
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraints             {col 26}{c |}
{res}{col 1}{text}         1.treatment = 0{col 26}{c |}{result}{space 2} .0948848{col 38}{space 1}   0.43{col 46}{space 3}0.710{col 54}{space 3}-.4216247{col 66}{space 3} .5614497
{col 1}{text}  1.RSES_std_upper50 = 0{col 26}{c |}{result}{space 2}-.5067505{col 38}{space 1}  -3.63{col 46}{space 3}0.002{col 54}{space 3}-.7931992{col 66}{space 3}-.2202677
{col 1}{text}            1.treatment#{col 26}{c |}
{res}{col 1}{text}  1.RSES_std_upper50 = 0{col 26}{c |}{result}{space 2} .6801748{col 38}{space 1}   2.84{col 46}{space 3}0.008{col 54}{space 3} .1765929{col 66}{space 3} 1.179148
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix RSES_difference = r(table)
{txt}
{com}. 
. wildbootstrap reg CPCS_std i.treatment##i.CPCS_std_upper50, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:1.treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:1.treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:1.CPCS_std_upper50 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:1.CPCS_std_upper50}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:1.treatment#1.CPCS_std_upper50 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:1.treatment#1.CPCS_std_upper50}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}236
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.2
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraints             {col 26}{c |}
{res}{col 1}{text}         1.treatment = 0{col 26}{c |}{result}{space 2} .3378141{col 38}{space 1}   1.31{col 46}{space 3}0.226{col 54}{space 3}-.2401429{col 66}{space 3} .8560969
{col 1}{text}  1.CPCS_std_upper50 = 0{col 26}{c |}{result}{space 2}-.2857744{col 38}{space 1}  -1.34{col 46}{space 3}0.210{col 54}{space 3}-.7190431{col 66}{space 3} .1735032
{col 1}{text}            1.treatment#{col 26}{c |}
{res}{col 1}{text}  1.CPCS_std_upper50 = 0{col 26}{c |}{result}{space 2} .2896806{col 38}{space 1}   1.01{col 46}{space 3}0.324{col 54}{space 3}-.3368099{col 66}{space 3} .8833211
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix CPCS_difference = r(table)
{txt}
{com}. 
. 
. // significant level
. 
. local outcome followup_cog_std RSES_std CPCS_std
{txt}
{com}. local hetero upper lower
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. foreach h in `hetero'{c -(}
{txt}  3{com}.                 if r2_`dep'_`h'_pv[1,1]<=0.01 {c -(}
{txt}  4{com}.                         local star_`dep'_`h' %3s "***"
{txt}  5{com}.                 {c )-}
{txt}  6{com}.                 else if (r2_`dep'_`h'_pv[1,1]>0.01) & (r2_`dep'_`h'_pv[1,1]<=0.05) {c -(}
{txt}  7{com}.                         local star_`dep'_`h' %2s "**"
{txt}  8{com}.                 {c )-}
{txt}  9{com}.                 else if (r2_`dep'_`h'_pv[1,1]>0.05) & (r2_`dep'_`h'_pv[1,1]<=0.10) {c -(}
{txt} 10{com}.                         local star_`dep'_`h' %1s "*"
{txt} 11{com}.                 {c )-}
{txt} 12{com}.                 else {c -(}
{txt} 13{com}.                         local star_`dep'_`h'  ""
{txt} 14{com}.                 {c )-}
{txt} 15{com}. {c )-} 
{txt} 16{com}. {c )-}
{txt}
{com}. 
. /// Table
> tempname hh2
{txt}
{com}. file open `hh2' using "$path_output/hetero.tex", write replace
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "% Author: Kazuma Takakura" _newline
{txt}
{com}. file write `hh2' "% Date: `c(current_date)'" _newline
{txt}
{com}. file write `hh2' "% Time: `c(current_time)'" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. 
. file write `hh2' "\begin{c -(}table{c )-}[h!]\footnotesize" _newline
{txt}
{com}. file write `hh2' "  \centering" _newline
{txt}
{com}. file write `hh2' "  \caption{c -(}Heterogeneity among Baseline Abilites{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:hetero{c )-}" _newline
{txt}
{com}. file write `hh2' "\scalebox{c -(}1{c )-}{c -(}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}threeparttable{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "\begin{c -(}tabular{c )-}{c -(}lccc{c )-}\toprule" _newline
{txt}
{com}. 
. file write `hh2' "  & Top 50\%^{c -(}b{c )-}  & Bottom 50\%^{c -(}b{c )-} & Differences  \\\midrule" _newline
{txt}
{com}. file write `hh2' "\multicolumn{c -(}4{c )-}{c -(}c{c )-}{c -(}Panel A: Rapid math test score^{c -(}a{c )-}{c )-}\\\midrule" _newline
{txt}
{com}. file write `hh2' " Treatment & " %04.3f (r2_followup_cog_std_upper_mean[1,1]) `star_followup_cog_std_upper' "  & " %04.3f (r2_followup_cog_std_lower_mean[1,1]) `star_followup_cog_std_lower' " &  " %04.3f (cog_difference[1,3]) " \\" _newline
{txt}
{com}. file write `hh2' " & ( " %04.3f (r2_followup_cog_std_upper_pv[1,1]) " ) & ( " %04.3f (r2_followup_cog_std_lower_pv[1,1]) " ) & ( " %04.3f (cog_difference[3,3]) " ) \\ " _newline
{txt}
{com}. file write `hh2' " Observation &  " %02.0f ( n_cog_u ) " & " %02.0f ( n_cog_l ) " &  \\ " _newline
{txt}
{com}. file write `hh2' " N of clusters &  " %02.0f ( n_clust_cog_u ) " & " %02.0f ( n_clust_cog_l ) " &  \\\midrule " _newline
{txt}
{com}. 
. file write `hh2' "\multicolumn{c -(}4{c )-}{c -(}c{c )-}{c -(}Panel B: RSES score^{c -(}a{c )-}{c )-}\\\midrule" _newline
{txt}
{com}. file write `hh2' " Treatment & " %04.3f (r2_RSES_std_upper_mean[1,1]) `star_RSES_std_upper' "  & " %04.3f (r2_RSES_std_lower_mean[1,1]) `star_RSES_std_lower' " &  " %04.3f (RSES_difference[1,3]) " ** \\" _newline
{txt}
{com}. file write `hh2' " & ( " %04.3f (r2_RSES_std_upper_pv[1,1]) " ) & ( " %04.3f (r2_RSES_std_lower_pv[1,1]) " ) & ( " %04.3f (RSES_difference[3,3]) " ) \\ " _newline
{txt}
{com}. file write `hh2' " Observation &  " %02.0f ( n_RSES_std_u ) " & " %02.0f ( n_RSES_std_l ) " &  \\ " _newline
{txt}
{com}. file write `hh2' " N of clusters &  " %02.0f ( n_clust_RSES_std_u ) " & " %02.0f ( n_clust_RSES_std_l ) " &  \\\midrule " _newline
{txt}
{com}. 
. file write `hh2' "\multicolumn{c -(}4{c )-}{c -(}c{c )-}{c -(}Panel C: CPCS score^{c -(}a{c )-}{c )-}\\\midrule" _newline
{txt}
{com}. file write `hh2' " Treatment & " %04.3f (r2_CPCS_std_upper_mean[1,1]) `star_CPCS_std_upper' "  & " %04.3f (r2_CPCS_std_lower_mean[1,1]) `star_CPCS_std_lower' " &  " %04.3f (CPCS_difference[1,3]) " \\" _newline
{txt}
{com}. file write `hh2' " & ( " %04.3f (r2_CPCS_std_upper_pv[1,1]) " ) & ( " %04.3f (r2_CPCS_std_lower_pv[1,1]) " ) & ( " %04.3f (CPCS_difference[3,3]) " ) \\ " _newline
{txt}
{com}. file write `hh2' " Observation &  " %02.0f ( n_CPCS_std_u ) " & " %02.0f ( n_CPCS_std_l ) " &  \\ " _newline
{txt}
{com}. file write `hh2' " N of clusters &  " %02.0f ( n_clust_CPCS_std_u ) " & " %02.0f ( n_clust_CPCS_std_l ) " &  \\\midrule " _newline
{txt}
{com}. 
. file write `hh2' "\end{c -(}tabular{c )-}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\item (a) Dependent variables are standardized using the average and variance of the whole follow-up sample in the February 2022 survey." _newline
{txt}
{com}. file write `hh2' "\item (b) Cutoffs are created based on whether their ability to perform each item at the baseline was higher or lower than the median. " _newline
{txt}
{com}. file write `hh2' "\item (c) Wild cluster bootstrap p-values are reported within parentheses. Clusters are schools at the baseline. " _newline
{txt}
{com}. file write `hh2' "\item (d) $^*$ Significant at 10\% level; $^{c -(}**{c )-}$ significant at 5\% level; $^{c -(}***{c )-}$ significant at 1\% level. " _newline
{txt}
{com}. file write `hh2' "\end{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}threeparttable{c )-}" _newline
{txt}
{com}. file write `hh2' "{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:addlabel{c )-}%" _newline
{txt}
{com}. file write `hh2' "\end{c -(}table{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. file close `hh2'
{txt}
{com}. 
. 
{txt}end of do-file

{com}. 
. do "$path_do/2_table_5.do"
{txt}
{com}. * This is the do file to create "Table 5. Heterogeneity by Baseline Abilites (Math and CPCS)"
. set seed 123
{txt}
{com}. 
. use "$path_data/temp/followup_student_baseline_score_missing_dummy", replace
{txt}
{com}. 
. egen DT_score_pre_std_med = median(DT_score_pre_std)
{txt}
{com}. gen DT_score_pre_std_upper50 = 1 if DT_score_pre_std>DT_score_pre_std_med
{txt}(121 missing values generated)

{com}. recode DT_score_pre_std_upper50 (.=0)
{txt}(121 changes made to {bf:DT_score_pre_std_upper50})

{com}. 
. egen rosen_pre_std_med = median(rosen_pre_std)
{txt}
{com}. gen rosen_pre_std_upper50 = 1 if rosen_pre_std>rosen_pre_std_med
{txt}(128 missing values generated)

{com}. recode rosen_pre_std_upper50 (.=0)
{txt}(128 changes made to {bf:rosen_pre_std_upper50})

{com}. rename rosen_pre_std_upper50 RSES_std_upper50
{res}{txt}
{com}. 
. egen cpcs_pre_std_med = median(cpcs_pre_std)
{txt}
{com}. gen cpcs_pre_std_upper50 = 1 if cpcs_pre_std>cpcs_pre_std_med
{txt}(135 missing values generated)

{com}. recode cpcs_pre_std_upper50 (.=0)
{txt}(135 changes made to {bf:cpcs_pre_std_upper50})

{com}. rename cpcs_pre_std_upper50 CPCS_std_upper50
{res}{txt}
{com}. 
. 
. /// Cognitive
> wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 59
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 22
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2397567{col 38}{space 1}  -0.86{col 46}{space 3}0.444{col 54}{space 3} -.835023{col 66}{space 3} .4309843
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_rses_u = e(N)
{txt}
{com}. scalar n_clust_cog_u_rses_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.3459862{col 38}{space 1}  -1.14{col 46}{space 3}0.246{col 54}{space 3}-.9646946{col 66}{space 3} .2967857
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_rses_l = e(N)
{txt}
{com}. scalar n_clust_cog_u_rses_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2419353{col 38}{space 1}  -0.87{col 46}{space 3}0.390{col 54}{space 3}-.8433267{col 66}{space 3} .3920268
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_rses_u = e(N)
{txt}
{com}. scalar n_clust_cog_l_rses_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0781293{col 38}{space 1}  -0.29{col 46}{space 3}0.794{col 54}{space 3}-.6038382{col 66}{space 3} .5443816
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_rses_l = e(N)
{txt}
{com}. scalar n_clust_cog_l_rses_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 55
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0533319{col 38}{space 1}   0.17{col 46}{space 3}0.872{col 54}{space 3}-.6242397{col 66}{space 3} .7907538
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_cpcs_u = e(N)
{txt}
{com}. scalar n_clust_cog_u_cpcs_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 67
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.5660393{col 38}{space 1}  -1.84{col 46}{space 3}0.092{col 54}{space 3}-1.245036{col 66}{space 3} .1419984
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_cpcs_l = e(N)
{txt}
{com}. scalar n_clust_cog_u_cpcs_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0295186{col 38}{space 1}   0.10{col 46}{space 3}0.942{col 54}{space 3}-.6165229{col 66}{space 3} .6514795
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_cpcs_u = e(N)
{txt}
{com}. scalar n_clust_cog_l_cpcs_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 68
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  -.19243{col 38}{space 1}  -0.73{col 46}{space 3}0.480{col 54}{space 3}-.7462179{col 66}{space 3} .3675341
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_cpcs_l = e(N)
{txt}
{com}. scalar n_clust_cog_l_cpcs_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. 
. 
. /// RSES
> wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .9579135{col 38}{space 1}   3.36{col 46}{space 3}0.000{col 54}{space 3} .3158411{col 66}{space 3} 1.651529
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_u_cog_u = e(N)
{txt}
{com}. scalar n_clust_rses_u_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 61
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0615952{col 38}{space 1}   0.32{col 46}{space 3}0.748{col 54}{space 3} -.335182{col 66}{space 3} .4398791
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_l_cog_u = e(N)
{txt}
{com}. scalar n_clust_rses_l_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:29})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .4920182{col 38}{space 1}   1.44{col 46}{space 3}0.152{col 54}{space 3}-.1956882{col 66}{space 3} 1.132075
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_u_cog_l = e(N)
{txt}
{com}. scalar n_clust_rses_u_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1388312{col 38}{space 1}   0.42{col 46}{space 3}0.748{col 54}{space 3}-.6586927{col 66}{space 3} .8819884
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_l_cog_l = e(N)
{txt}
{com}. scalar n_clust_rses_l_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 52
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 20
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .6664341{col 38}{space 1}   3.26{col 46}{space 3}0.004{col 54}{space 3} .2256924{col 66}{space 3}  1.16988
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3847293{col 38}{space 1}   1.53{col 46}{space 3}0.150{col 54}{space 3}-.1439999{col 66}{space 3} .9723153
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2062219{col 38}{space 1}   0.47{col 46}{space 3}0.630{col 54}{space 3}-.9392839{col 66}{space 3} 1.108811
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 66
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3332931{col 38}{space 1}   0.96{col 46}{space 3}0.416{col 54}{space 3}-.4864367{col 66}{space 3} 1.101715
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. 
. 
. /// CPCS
> 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} 1.020132{col 38}{space 1}   3.66{col 46}{space 3}0.002{col 54}{space 3} .4199485{col 66}{space 3} 1.782028
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 61
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0665639{col 38}{space 1}   0.34{col 46}{space 3}0.706{col 54}{space 3}-.3338298{col 66}{space 3} .4994486
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .5458399{col 38}{space 1}   1.63{col 46}{space 3}0.112{col 54}{space 3}-.2035501{col 66}{space 3} 1.209728
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1431476{col 38}{space 1}   0.41{col 46}{space 3}0.700{col 54}{space 3}-.6565782{col 66}{space 3} 1.032317
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:19})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 52
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 20
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  .774167{col 38}{space 1}   3.68{col 46}{space 3}0.006{col 54}{space 3}  .320576{col 66}{space 3} 1.297191
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_u_cog_u = e(N)
{txt}
{com}. scalar n_clust_cpcs_u_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3575321{col 38}{space 1}   1.51{col 46}{space 3}0.154{col 54}{space 3} -.150128{col 66}{space 3} .9146968
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_l_cog_u = e(N)
{txt}
{com}. scalar n_clust_cpcs_l_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2792052{col 38}{space 1}   0.63{col 46}{space 3}0.546{col 54}{space 3}-.9224898{col 66}{space 3} 1.348458
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_u_cog_l = e(N)
{txt}
{com}. scalar n_clust_cpcs_u_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 66
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}   .33148{col 38}{space 1}   0.95{col 46}{space 3}0.420{col 54}{space 3}-.5052153{col 66}{space 3} 1.053721
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_l_cog_l = e(N)
{txt}
{com}. scalar n_clust_cpcs_l_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. // significant level
. 
. 
. local outcome cog rses cpcs
{txt}
{com}. local hetero1 cogU cogL
{txt}
{com}. local hetero2 rsesU rsesL
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. foreach h1 in `hetero1'{c -(}
{txt}  3{com}. foreach h2 in `hetero2'{c -(}
{txt}  4{com}.                 if `dep'_`h1'_`h2'_pv[1,1]<=0.01 {c -(}
{txt}  5{com}.                         local star_`dep'_`h1'_`h2' %3s "***"
{txt}  6{com}.                 {c )-}
{txt}  7{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.01) & (`dep'_`h1'_`h2'_pv[1,1]<=0.05) {c -(}
{txt}  8{com}.                         local star_`dep'_`h1'_`h2' %2s "**"
{txt}  9{com}.                 {c )-}
{txt} 10{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.05) & (`dep'_`h1'_`h2'_pv[1,1]<=0.10) {c -(}
{txt} 11{com}.                         local star_`dep'_`h1'_`h2' %1s "*"
{txt} 12{com}.                 {c )-}
{txt} 13{com}.                 else {c -(}
{txt} 14{com}.                         local star_`dep'_`h1'_`h2'  ""
{txt} 15{com}.                 {c )-}
{txt} 16{com}. {c )-} 
{txt} 17{com}. {c )-}
{txt} 18{com}. {c )-}
{txt}
{com}. 
. local outcome cog rses cpcs
{txt}
{com}. local hetero1 cogU cogL
{txt}
{com}. local hetero2 cpcsU cpcsL
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. foreach h1 in `hetero1'{c -(}
{txt}  3{com}. foreach h2 in `hetero2'{c -(}
{txt}  4{com}.                 if `dep'_`h1'_`h2'_pv[1,1]<=0.01 {c -(}
{txt}  5{com}.                         local star_`dep'_`h1'_`h2' %3s "***"
{txt}  6{com}.                 {c )-}
{txt}  7{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.01) & (`dep'_`h1'_`h2'_pv[1,1]<=0.05) {c -(}
{txt}  8{com}.                         local star_`dep'_`h1'_`h2' %2s "**"
{txt}  9{com}.                 {c )-}
{txt} 10{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.05) & (`dep'_`h1'_`h2'_pv[1,1]<=0.10) {c -(}
{txt} 11{com}.                         local star_`dep'_`h1'_`h2' %1s "*"
{txt} 12{com}.                 {c )-}
{txt} 13{com}.                 else {c -(}
{txt} 14{com}.                         local star_`dep'_`h1'_`h2'  ""
{txt} 15{com}.                 {c )-}
{txt} 16{com}. {c )-} 
{txt} 17{com}. {c )-}
{txt} 18{com}. {c )-}
{txt}
{com}. 
. /// Table
> 
. tempname hh2
{txt}
{com}. file open `hh2' using "$path_output/hetero_2by2_CPCS.tex", write replace
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "% Author: Kazuma Takakura" _newline
{txt}
{com}. file write `hh2' "% Date: `c(current_date)'" _newline
{txt}
{com}. file write `hh2' "% Time: `c(current_time)'" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. 
. file write `hh2' "\begin{c -(}table{c )-}[h!]\footnotesize" _newline
{txt}
{com}. file write `hh2' "  \centering" _newline
{txt}
{com}. file write `hh2' "  \caption{c -(}Heterogeneity among Baseline Abilites (Math and CPCS){c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:hetero_cpcs{c )-}" _newline
{txt}
{com}. file write `hh2' "\scalebox{c -(}1{c )-}{c -(}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}threeparttable{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "\begin{c -(}tabular{c )-}{c -(}lccccccc{c )-}\toprule" _newline
{txt}
{com}. 
. file write `hh2' " Dependent Variable & \multicolumn{c -(}2{c )-}{c -(}c{c )-}{c -(}Baseline^{c -(}b{c )-}{c )-} & Difference & Obs & N of clusters  \\\midrule\midrule" _newline
{txt}
{com}. file write `hh2' " Rapid math test score^{c -(}a{c )-} & Math Top 50\%  & CPCS Top 50\% & " %04.3f (cog_cogU_rsesU_mean[1,1]) `star_cog_cogU_cpcsU' " & " %02.0f ( n_cog_u_cpcs_u ) " & " %02.0f ( n_clust_cog_u_cpcs_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogU_cpcsU_pv[1,1]) " )  & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cog_cogU_cpcsL_mean[1,1]) `star_cog_cogU_cpcsL' " & " %02.0f ( n_cog_u_cpcs_l ) " & " %02.0f ( n_clust_cog_u_cpcs_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogU_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & Math Bottom 50\%  & CPCS Top 50\% & " %04.3f (cog_cogL_cpcsU_mean[1,1]) `star_cog_cogL_cpcsU' " & " %02.0f ( n_cog_l_cpcs_u ) " & " %02.0f ( n_clust_cog_l_cpcs_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogL_cpcsU_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cog_cogL_cpcsL_mean[1,1]) `star_cog_cogL_cpcsL' " & " %02.0f ( n_cog_l_cpcs_l ) " & " %02.0f ( n_clust_cog_l_cpcs_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogL_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. 
. file write `hh2' " CPCS score^{c -(}a{c )-} & Math Top 50\%  & CPCS Top 50\% & " %04.3f (cpcs_cogU_cpcsU_mean[1,1]) `star_cpcs_cogU_cpcsU' " & " %02.0f ( n_cpcs_u_cog_u ) " & " %02.0f ( n_clust_cpcs_u_cog_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cpcs_cogU_cpcsU_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cpcs_cogU_cpcsL_mean[1,1]) `star_cpcs_cogU_cpcsL' " & " %02.0f ( n_cpcs_l_cog_u ) " & " %02.0f ( n_clust_cpcs_l_cog_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &       & & ( " %04.3f (cpcs_cogU_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & Math Bottom 50\%  & CPCS Top 50\% & " %04.3f (cpcs_cogL_cpcsU_mean[1,1]) `star_cpcs_cogL_cpcsU' " & " %02.0f ( n_cpcs_u_cog_l ) " & " %02.0f ( n_clust_cpcs_u_cog_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cpcs_cogL_cpcsU_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cpcs_cogL_cpcsL_mean[1,1]) `star_cpcs_cogL_cpcsL' " & " %02.0f ( n_cpcs_l_cog_l ) " & " %02.0f ( n_clust_cpcs_l_cog_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &       & & ( " %04.3f (cpcs_cogL_cpcsL_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. 
. file write `hh2' "\\\midrule" _newline
{txt}
{com}. file write `hh2' "\end{c -(}tabular{c )-}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\item (a) Dependent variables are standardized using the average and variance of the whole follow-up sample in the February 2022 survey. " _newline
{txt}
{com}. file write `hh2' "\item (b) Cutoffs are created based on whether their ability to perform each item at the baseline was higher or lower than the median. " _newline
{txt}
{com}. file write `hh2' "\item (c) Wild cluster bootstrap p-values are reported within parentheses. Clusters are schools at the baseline." _newline
{txt}
{com}. file write `hh2' "\item (d) $^*$ Significant at 10\% level; $^{c -(}**{c )-}$ significant at 5\% level; $^{c -(}***{c )-}$ significant at 1\% level. " _newline
{txt}
{com}. file write `hh2' "\end{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}threeparttable{c )-}" _newline
{txt}
{com}. file write `hh2' "{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:addlabel{c )-}%" _newline
{txt}
{com}. file write `hh2' "\end{c -(}table{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. file close `hh2'
{txt}
{com}. 
{txt}end of do-file

{com}. 
. 
. do "$path_do/3_figure_B1.do"
{txt}
{com}. * This is the do file to create "Figure B1. Propensity Score Overlap"
. 
. use "$path_data/temp/followup_student_parents_matched", replace
{txt}
{com}. 
. // variable
. gen gend = q1d - 1
{txt}
{com}. 
. 
. local controls DT_score_pre_std_missing_0 rosen_pre_std_missing_0 cpcs_pre_std_missing_0 i.grade gend branch1 branch2 branch3 income_source1 income_source2 income_source3 income_source4 last_income_per_member hhmember hhheadage hhheadeduyear phone_survey age_tchr
{txt}
{com}. 
. 
. // PS 
. logit treatment `controls'

{res}{txt}Iteration 0:{space 2}Log likelihood = {res:-163.86073}  
Iteration 1:{space 2}Log likelihood = {res:-139.65506}  
Iteration 2:{space 2}Log likelihood = {res:-139.28345}  
Iteration 3:{space 2}Log likelihood = {res:-139.28041}  
Iteration 4:{space 2}Log likelihood = {res:-139.28041}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:243}
{txt}{col 57}{lalign 13:LR chi2({res:18})}{col 70} = {res}{ralign 6:49.16}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0001}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-139.28041}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.1500}

{txt}{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                 treatment{col 28}{c |} Coefficient{col 40}  Std. err.{col 52}      z{col 60}   P>|z|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
DT_score_pre_std_missing_0 {c |}{col 28}{res}{space 2}-.2108634{col 40}{space 2} .1559605{col 51}{space 1}   -1.35{col 60}{space 3}0.176{col 68}{space 4}-.5165404{col 81}{space 3} .0948136
{txt}{space 3}rosen_pre_std_missing_0 {c |}{col 28}{res}{space 2}-1.156243{col 40}{space 2} .3670308{col 51}{space 1}   -3.15{col 60}{space 3}0.002{col 68}{space 4} -1.87561{col 81}{space 3} -.436876
{txt}{space 4}cpcs_pre_std_missing_0 {c |}{col 28}{res}{space 2} 1.363271{col 40}{space 2}  .373272{col 51}{space 1}    3.65{col 60}{space 3}0.000{col 68}{space 4} .6316711{col 81}{space 3} 2.094871
{txt}{space 19}4.grade {c |}{col 28}{res}{space 2} .2601036{col 40}{space 2} .3425485{col 51}{space 1}    0.76{col 60}{space 3}0.448{col 68}{space 4}-.4112792{col 81}{space 3} .9314864
{txt}{space 22}gend {c |}{col 28}{res}{space 2} .0431333{col 40}{space 2} .3136935{col 51}{space 1}    0.14{col 60}{space 3}0.891{col 68}{space 4}-.5716947{col 81}{space 3} .6579613
{txt}{space 19}branch1 {c |}{col 28}{res}{space 2}-.8730908{col 40}{space 2} .5101577{col 51}{space 1}   -1.71{col 60}{space 3}0.087{col 68}{space 4}-1.872982{col 81}{space 3} .1267998
{txt}{space 19}branch2 {c |}{col 28}{res}{space 2} .3672116{col 40}{space 2} .5857284{col 51}{space 1}    0.63{col 60}{space 3}0.531{col 68}{space 4} -.780795{col 81}{space 3} 1.515218
{txt}{space 19}branch3 {c |}{col 28}{res}{space 2}-.9819822{col 40}{space 2} .4213023{col 51}{space 1}   -2.33{col 60}{space 3}0.020{col 68}{space 4} -1.80772{col 81}{space 3}-.1562449
{txt}{space 12}income_source1 {c |}{col 28}{res}{space 2}-1.392694{col 40}{space 2} 1.198015{col 51}{space 1}   -1.16{col 60}{space 3}0.245{col 68}{space 4} -3.74076{col 81}{space 3} .9553711
{txt}{space 12}income_source2 {c |}{col 28}{res}{space 2}-.1140878{col 40}{space 2} .9307761{col 51}{space 1}   -0.12{col 60}{space 3}0.902{col 68}{space 4}-1.938375{col 81}{space 3}   1.7102
{txt}{space 12}income_source3 {c |}{col 28}{res}{space 2}-.4115209{col 40}{space 2} .8999542{col 51}{space 1}   -0.46{col 60}{space 3}0.647{col 68}{space 4}-2.175399{col 81}{space 3} 1.352357
{txt}{space 12}income_source4 {c |}{col 28}{res}{space 2} 1.910332{col 40}{space 2} 2.758545{col 51}{space 1}    0.69{col 60}{space 3}0.489{col 68}{space 4}-3.496317{col 81}{space 3} 7.316982
{txt}{space 4}last_income_per_member {c |}{col 28}{res}{space 2}-.0001351{col 40}{space 2} .0001481{col 51}{space 1}   -0.91{col 60}{space 3}0.362{col 68}{space 4}-.0004253{col 81}{space 3} .0001551
{txt}{space 18}hhmember {c |}{col 28}{res}{space 2} .1845254{col 40}{space 2} .1265051{col 51}{space 1}    1.46{col 60}{space 3}0.145{col 68}{space 4}  -.06342{col 81}{space 3} .4324708
{txt}{space 17}hhheadage {c |}{col 28}{res}{space 2}-.0072255{col 40}{space 2} .0169493{col 51}{space 1}   -0.43{col 60}{space 3}0.670{col 68}{space 4}-.0404454{col 81}{space 3} .0259945
{txt}{space 13}hhheadeduyear {c |}{col 28}{res}{space 2}-.0778674{col 40}{space 2} .0471652{col 51}{space 1}   -1.65{col 60}{space 3}0.099{col 68}{space 4}-.1703095{col 81}{space 3} .0145748
{txt}{space 14}phone_survey {c |}{col 28}{res}{space 2}-.0776531{col 40}{space 2} .3471772{col 51}{space 1}   -0.22{col 60}{space 3}0.823{col 68}{space 4}-.7581078{col 81}{space 3} .6028016
{txt}{space 18}age_tchr {c |}{col 28}{res}{space 2}-.0365818{col 40}{space 2} .0244024{col 51}{space 1}   -1.50{col 60}{space 3}0.134{col 68}{space 4}-.0844097{col 81}{space 3}  .011246
{txt}{space 21}_cons {c |}{col 28}{res}{space 2}  2.00163{col 40}{space 2} 1.433552{col 51}{space 1}    1.40{col 60}{space 3}0.163{col 68}{space 4}-.8080801{col 81}{space 3} 4.811341
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. predict ps
{txt}(option {bf:pr} assumed; Pr(treatment))

{com}. label define treatlabel 1 "treatment" 0 "control"
{txt}
{com}. label val treatment treatlabel
{txt}
{com}. * hist ps, by(treatment) bin(30)
. * graph save "`pardir'/fig_ps.png", replace
. 
. ksmirnov ps, by(treatment)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
control            {res}  0.3634       0.000
{txt}treatment          {res}  0.0000       1.000
{txt}Combined K-S       {res}  0.3634       0.000
{txt}
{com}. 
. twoway (kdensity ps if treatment == 0, bwidth(0.01) lcolor(blue)) (kdensity ps if treatment == 1, bwidth(0.01) lcolor(red)), ///
>        legend(label(1 "Control") label(2 "Treatment")) ///
>            ytitle("density") xtitle("propensity score")
{res}{txt}
{com}. graph save "$path_output/fig_ps.png", replace
{res}{txt}file {bf:/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/outputs/fig_ps.png} saved as .gph format

{com}. 
{txt}end of do-file

{com}. 
. do "$path_do/3_table_B1.do"
{txt}
{com}. * This is the do file to create "Table B1. Summary Statistics (Matched Sample)"
. set seed 123
{txt}
{com}. 
. use "$path_data/temp/followup_student_parents_matched", clear
{txt}
{com}. 
. gen gend = q1d - 1
{txt}
{com}. 
. local controls DT_score_pre_std_missing_0 rosen_pre_std_missing_0 cpcs_pre_std_missing_0 i.grade gend branch1 branch2 branch3 income_source1 income_source2 income_source3 income_source4 last_income_per_member hhmember hhheadage hhheadeduyear phone_survey age_tchr
{txt}
{com}. teffects psmatch (followup_cog_std) (treatment `controls') 
{res}
{txt}Treatment-effects estimation{col 48}Number of obs {col 67}= {res}       243
{txt:Estimator}{col 16}:{res: propensity-score matching}{col 48}{txt:Matches: requested }{col 67}{txt:=}          1
{txt:Outcome model}{col 16}:{res: matching}{txt}{col 63}min {col 67}= {res}         1
{txt:Treatment model}{col 16}:{res: logit}{col 63}{txt:max }{col 67}{txt:=}          1
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}   AI robust
{col 1}followup_c~d{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ATE          {txt}{c |}
{space 3}treatment {c |}
{space 3}(1 vs 0)  {c |}{col 14}{res}{space 2}-.2234479{col 26}{space 2} .1415597{col 37}{space 1}   -1.58{col 46}{space 3}0.114{col 54}{space 4}-.5008998{col 67}{space 3} .0540039
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. psmatch2 treatment `controls', outcome(followup_cog_std) noreplacement
{res}
{txt}{col 1}Probit regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:243}
{txt}{col 57}{lalign 13:LR chi2({res:18})}{col 70} = {res}{ralign 6:49.32}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0001}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-139.20088}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.1505}

{txt}{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                 treatment{col 28}{c |} Coefficient{col 40}  Std. err.{col 52}      z{col 60}   P>|z|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
DT_score_pre_std_missing_0 {c |}{col 28}{res}{space 2}-.1241732{col 40}{space 2} .0940284{col 51}{space 1}   -1.32{col 60}{space 3}0.187{col 68}{space 4}-.3084655{col 81}{space 3} .0601191
{txt}{space 3}rosen_pre_std_missing_0 {c |}{col 28}{res}{space 2}-.7085829{col 40}{space 2} .2197816{col 51}{space 1}   -3.22{col 60}{space 3}0.001{col 68}{space 4}-1.139347{col 81}{space 3}-.2778189
{txt}{space 4}cpcs_pre_std_missing_0 {c |}{col 28}{res}{space 2} .8194579{col 40}{space 2} .2199089{col 51}{space 1}    3.73{col 60}{space 3}0.000{col 68}{space 4} .3884443{col 81}{space 3} 1.250471
{txt}{space 19}4.grade {c |}{col 28}{res}{space 2} .1558284{col 40}{space 2} .2036202{col 51}{space 1}    0.77{col 60}{space 3}0.444{col 68}{space 4}-.2432597{col 81}{space 3} .5549166
{txt}{space 22}gend {c |}{col 28}{res}{space 2} .0277631{col 40}{space 2}  .188691{col 51}{space 1}    0.15{col 60}{space 3}0.883{col 68}{space 4}-.3420644{col 81}{space 3} .3975907
{txt}{space 19}branch1 {c |}{col 28}{res}{space 2}-.5388879{col 40}{space 2} .3027873{col 51}{space 1}   -1.78{col 60}{space 3}0.075{col 68}{space 4} -1.13234{col 81}{space 3} .0545643
{txt}{space 19}branch2 {c |}{col 28}{res}{space 2} .1721494{col 40}{space 2}  .342091{col 51}{space 1}    0.50{col 60}{space 3}0.615{col 68}{space 4}-.4983367{col 81}{space 3} .8426354
{txt}{space 19}branch3 {c |}{col 28}{res}{space 2}-.6071847{col 40}{space 2} .2518722{col 51}{space 1}   -2.41{col 60}{space 3}0.016{col 68}{space 4}-1.100845{col 81}{space 3}-.1135242
{txt}{space 12}income_source1 {c |}{col 28}{res}{space 2}-.9123512{col 40}{space 2} .7367166{col 51}{space 1}   -1.24{col 60}{space 3}0.216{col 68}{space 4}-2.356289{col 81}{space 3} .5315867
{txt}{space 12}income_source2 {c |}{col 28}{res}{space 2}-.0941421{col 40}{space 2} .5682751{col 51}{space 1}   -0.17{col 60}{space 3}0.868{col 68}{space 4}-1.207941{col 81}{space 3} 1.019657
{txt}{space 12}income_source3 {c |}{col 28}{res}{space 2}-.2974151{col 40}{space 2} .5481452{col 51}{space 1}   -0.54{col 60}{space 3}0.587{col 68}{space 4} -1.37176{col 81}{space 3} .7769297
{txt}{space 12}income_source4 {c |}{col 28}{res}{space 2} 1.298512{col 40}{space 2}  1.68612{col 51}{space 1}    0.77{col 60}{space 3}0.441{col 68}{space 4}-2.006222{col 81}{space 3} 4.603246
{txt}{space 4}last_income_per_member {c |}{col 28}{res}{space 2}-.0000821{col 40}{space 2} .0000869{col 51}{space 1}   -0.94{col 60}{space 3}0.345{col 68}{space 4}-.0002524{col 81}{space 3} .0000883
{txt}{space 18}hhmember {c |}{col 28}{res}{space 2} .1045404{col 40}{space 2} .0745649{col 51}{space 1}    1.40{col 60}{space 3}0.161{col 68}{space 4}-.0416041{col 81}{space 3} .2506849
{txt}{space 17}hhheadage {c |}{col 28}{res}{space 2}-.0041108{col 40}{space 2}  .010155{col 51}{space 1}   -0.40{col 60}{space 3}0.686{col 68}{space 4}-.0240141{col 81}{space 3} .0157926
{txt}{space 13}hhheadeduyear {c |}{col 28}{res}{space 2}-.0474059{col 40}{space 2}    .0283{col 51}{space 1}   -1.68{col 60}{space 3}0.094{col 68}{space 4} -.102873{col 81}{space 3} .0080611
{txt}{space 14}phone_survey {c |}{col 28}{res}{space 2}-.0370505{col 40}{space 2} .2121298{col 51}{space 1}   -0.17{col 60}{space 3}0.861{col 68}{space 4}-.4528173{col 81}{space 3} .3787163
{txt}{space 18}age_tchr {c |}{col 28}{res}{space 2}-.0222608{col 40}{space 2} .0146599{col 51}{space 1}   -1.52{col 60}{space 3}0.129{col 68}{space 4}-.0509937{col 81}{space 3} .0064721
{txt}{space 21}_cons {c |}{col 28}{res}{space 2} 1.289201{col 40}{space 2} .8634647{col 51}{space 1}    1.49{col 60}{space 3}0.135{col 68}{space 4}-.4031584{col 81}{space 3} 2.981561
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}{hline 28}{c TT}{hline 59}
        Variable     Sample {c |}    Treated     Controls   Difference         S.E.   T-stat
{hline 28}{c +}{hline 59}
followup_cog_std  Unmatched {c |}{res}-.092040852   .136183089  -.228223941   .130213149    -1.75
{txt}{col 17}        ATT {c |}{res}-.252378217   .136183089  -.388561306   .141378263    -2.75
{txt}{hline 28}{c +}{hline 59}
Note: S.E. does not take into account that the propensity score is estimated.

 psmatch2: {c |}   psmatch2: Common
 Treatment {c |}        support
assignment {c |} Off suppo  On suppor {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
 Untreated {c |}{res}         0         98 {txt}{c |}{res}        98 
{txt}   Treated {c |}{res}        47         98 {txt}{c |}{res}       145 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}        47        196 {txt}{c |}{res}       243 
{txt}
{com}. gen psmattrition = 1 if _support!=1
{txt}(196 missing values generated)

{com}. recode psmattrition (.=0)
{txt}(196 changes made to {bf:psmattrition})

{com}. keep if psmattrition == 0
{txt}(47 observations deleted)

{com}. 
. /// Varable Selection
> /// Baseline
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat DT_score_pre_std rosen_pre_std cpcs_pre_std if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_bl = r(StatTotal)
{txt}  5{com}. 
. tabstat DT_score_pre_std rosen_pre_std cpcs_pre_std if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_bl = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}       97        98        98
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   DT_score_p~d  rosen_pre_~d  cpcs_pre_std
N {res}           97            98            98
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}       95        98        98
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   DT_score_p~d  rosen_pre_~d  cpcs_pre_std
N {res}           95            98            98
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res}-.1026564  -.002967 -.0076133
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      DT_score_p~d  rosen_pre_~d  cpcs_pre_std
Mean {res}   -.10265637    -.00296701    -.00761331
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .0475214 -.0566567 -.1990291
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      DT_score_p~d  rosen_pre_~d  cpcs_pre_std
Mean {res}    .04752144    -.05665667    -.19902912
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res}  1.09733  1.042325  .9794825
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    DT_score_p~d  rosen_pre_~d  cpcs_pre_std
SD {res}    1.0973297     1.0423246     .97948253
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} .9672202  1.038561  1.073121
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    DT_score_p~d  rosen_pre_~d  cpcs_pre_std
SD {res}    .96722024      1.038561     1.0731214
{reset}
{com}. 
. matrix n_bl = J(1,3,.)
{txt}
{com}. forvalues i = 1/3 {c -(}
{txt}  2{com}.         matrix n_bl[1,`i'] = n_tr_bl[1,`i'] + n_ct_bl[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in DT_score_pre_std rosen_pre_std cpcs_pre_std{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}192
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 31
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}6.2
{col 69}{txt}max{col 72} = {res} 12
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        DT_score_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.1501778{col 38}{space 1}  -0.66{col 46}{space 3}0.558{col 54}{space 3}-.5981203{col 66}{space 3} .3714887
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}196
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 32
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}6.1
{col 69}{txt}max{col 72} = {res} 12
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}           rosen_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0536897{col 38}{space 1}   0.24{col 46}{space 3}0.772{col 54}{space 3}-.3967629{col 66}{space 3} .4896943
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}196
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 32
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}6.1
{col 69}{txt}max{col 72} = {res} 12
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}            cpcs_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1914158{col 38}{space 1}   0.94{col 46}{space 3}0.382{col 54}{space 3}-.2392435{col 66}{space 3} .5943766
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. /// Family
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat hhmember hhheadage hhheadeduyear if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_parent = r(StatTotal)
{txt}  5{com}. 
. tabstat hhmember hhheadage hhheadeduyear if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_parent = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}       98        98        98
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
       hhmember     hhheadage  hhheadeduy~r
N {res}           98            98            98
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}       98        98        98
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
       hhmember     hhheadage  hhheadeduy~r
N {res}           98            98            98
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res} 4.357143  47.30612  2.663265
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
          hhmember     hhheadage  hhheadeduy~r
Mean {res}    4.3571429     47.306122     2.6632653
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res} 4.265306  46.68878  3.163265
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
          hhmember     hhheadage  hhheadeduy~r
Mean {res}    4.2653061     46.688776     3.1632653
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} 1.228946  8.804179  3.314896
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
        hhmember     hhheadage  hhheadeduy~r
SD {res}    1.2289464     8.8041791     3.3148957
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} 1.197515  9.408681  3.530993
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
        hhmember     hhheadage  hhheadeduy~r
SD {res}    1.1975148     9.4086808     3.5309935
{reset}
{com}. 
. matrix n_parent = J(1,3,.)
{txt}
{com}. forvalues i = 1/3 {c -(}
{txt}  2{com}.         matrix n_parent[1,`i'] = n_tr_parent[1,`i'] + n_ct_parent[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in hhmember hhheadage hhheadeduyear{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}196
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 32
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}6.1
{col 69}{txt}max{col 72} = {res} 12
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                hhmember{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0918367{col 38}{space 1}   0.46{col 46}{space 3}0.634{col 54}{space 3}-.2938608{col 66}{space 3} .5355229
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}196
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 32
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}6.1
{col 69}{txt}max{col 72} = {res} 12
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}               hhheadage{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .6173469{col 38}{space 1}   0.41{col 46}{space 3}0.690{col 54}{space 3} -2.50937{col 66}{space 3} 3.804477
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}196
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 32
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}6.1
{col 69}{txt}max{col 72} = {res} 12
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}           hhheadeduyear{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}      -.5{col 38}{space 1}  -1.15{col 46}{space 3}0.248{col 54}{space 3}-1.444642{col 66}{space 3} .3764706
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. 
. 
. /// School　attendance
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat q2a q2b q2c q2h if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_school = r(StatTotal)
{txt}  5{com}. 
. tabstat q2a q2b q2c q2h if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_school = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:N} {...}
{c |}{...}
 {res}       98        98        98        98
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
   q2a  q2b  q2c  q2h
N {res}  98   98   98   98
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:N} {...}
{c |}{...}
 {res}       98        98        98        98
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
   q2a  q2b  q2c  q2h
N {res}  98   98   98   98
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .5714286  9.632653  .0510204  .3571429
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
            q2a        q2b        q2c        q2h
Mean {res} .57142857  9.6326531  .05102041  .35714286
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .5306122  9.602041  .0408163  .4489796
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
            q2a        q2b        q2c        q2h
Mean {res} .53061224  9.6020408  .04081633  .44897959
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:SD} {...}
{c |}{...}
 {res}  .497416  1.009111  .2211707   .481621
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
          q2a        q2b        q2c        q2h
SD {res}   .497416  1.0091106  .22117069  .48162097
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:SD} {...}
{c |}{...}
 {res} .5016279  .8703571  .1988818  .4999474
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
          q2a        q2b        q2c        q2h
SD {res}  .5016279  .87035715  .19888179   .4999474
{reset}
{com}. 
. matrix n_school = J(1,4,.)
{txt}
{com}. forvalues i = 1/4 {c -(}
{txt}  2{com}.         matrix n_school[1,`i'] = n_tr_school[1,`i'] + n_ct_school[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in q2a q2b q2c q2h{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}196
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 32
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}6.1
{col 69}{txt}max{col 72} = {res} 12
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                     q2a{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0408163{col 38}{space 1}   0.43{col 46}{space 3}0.684{col 54}{space 3}  -.17342{col 66}{space 3} .2508407
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}196
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 32
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}6.1
{col 69}{txt}max{col 72} = {res} 12
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                     q2b{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0306122{col 38}{space 1}   0.15{col 46}{space 3}0.912{col 54}{space 3} -.446332{col 66}{space 3} .4699108
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}30{text}.{text} done{text} ({result:31})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}196
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 32
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}6.1
{col 69}{txt}max{col 72} = {res} 12
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                     q2c{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0102041{col 38}{space 1}   0.39{col 46}{space 3}0.670{col 54}{space 3}-.0429482{col 66}{space 3} .0597326
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}196
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 32
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}6.1
{col 69}{txt}max{col 72} = {res} 12
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                     q2h{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0918367{col 38}{space 1}  -0.98{col 46}{space 3}0.344{col 54}{space 3}-.2889882{col 66}{space 3}  .090284
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. /// Other study variable
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat tutor study_other study_affect_covid hometutoring onlineclass studymyself parentsteach if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_study = r(StatTotal)
{txt}  5{com}. 
. tabstat tutor study_other study_affect_covid hometutoring onlineclass studymyself parentsteach if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_study = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:N} {...}
{c |}{...}
 {res}       98        98        98        98        98        98        98
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
          tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
N {res}           98            98            98            98            98            98            98
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:N} {...}
{c |}{...}
 {res}       98        98        98        98        98        98        98
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
          tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
N {res}           98            98            98            98            98            98            98
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .3673469  .4693878  .6938776  .0816327  .0408163  .5612245  .0408163
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
             tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
Mean {res}    .36734694     .46938776     .69387755     .08163265     .04081633     .56122449     .04081633
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .4591837  .4285714  .6020408  .1428571  .1326531  .5306122  .1938776
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
             tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
Mean {res}    .45918367     .42857143     .60204082     .14285714     .13265306     .53061224     .19387755
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:SD} {...}
{c |}{...}
 {res} .4845607  .5016279  .4632508   .275212  .1988818  .4987888  .1988818
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
           tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
SD {res}     .4845607      .5016279      .4632508     .27521199     .19888179     .49878877     .19888179
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:SD} {...}
{c |}{...}
 {res} .5008934   .497416  .4919935  .3517262  .3409434  .5016279  .3973667
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
           tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
SD {res}    .50089337       .497416     .49199354     .35172623     .34094336      .5016279     .39736667
{reset}
{com}. 
. matrix n_study = J(1,8,.)
{txt}
{com}. forvalues i = 1/8 {c -(}
{txt}  2{com}.         matrix n_study[1,`i'] = n_tr_study[1,`i'] + n_ct_study[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in tutor study_other study_affect_covid hometutoring onlineclass studymyself parentsteach{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}196
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 32
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}6.1
{col 69}{txt}max{col 72} = {res} 12
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                   tutor{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0918367{col 38}{space 1}  -1.15{col 46}{space 3}0.278{col 54}{space 3}-.2595226{col 66}{space 3} .0751509
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}196
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 32
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}6.1
{col 69}{txt}max{col 72} = {res} 12
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}             study_other{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0408163{col 38}{space 1}   0.40{col 46}{space 3}0.722{col 54}{space 3}-.1823391{col 66}{space 3} .2570772
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}196
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 32
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}6.1
{col 69}{txt}max{col 72} = {res} 12
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}      study_affect_covid{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0918367{col 38}{space 1}   0.98{col 46}{space 3}0.344{col 54}{space 3}-.1181733{col 66}{space 3} .2778968
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}196
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 32
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}6.1
{col 69}{txt}max{col 72} = {res} 12
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}            hometutoring{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0612245{col 38}{space 1}  -1.57{col 46}{space 3}0.168{col 54}{space 3}-.1453856{col 66}{space 3} .0323677
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}196
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 32
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}6.1
{col 69}{txt}max{col 72} = {res} 12
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}             onlineclass{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0918367{col 38}{space 1}  -2.02{col 46}{space 3}0.052{col 54}{space 3}-.1849272{col 66}{space 3} .0005027
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}196
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 32
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}6.1
{col 69}{txt}max{col 72} = {res} 12
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}             studymyself{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0306122{col 38}{space 1}   0.33{col 46}{space 3}0.786{col 54}{space 3}-.1678675{col 66}{space 3} .2437695
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}196
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 32
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}6.1
{col 69}{txt}max{col 72} = {res} 12
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}            parentsteach{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.1530612{col 38}{space 1}  -3.33{col 46}{space 3}0.004{col 54}{space 3}-.2474297{col 66}{space 3}-.0567302
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. /// Cognitive
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat followup_cog_std if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_cog = r(StatTotal)
{txt}  5{com}. 
. tabstat followup_cog_std if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_cog = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}{ralign 12:Variable} {...}
{c |}         N
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res}       98
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
   followup_c~d
N {res}           98
{reset}
{ralign 12:Variable} {...}
{c |}         N
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res}       98
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
   followup_c~d
N {res}           98
{reset}
{ralign 12:Variable} {...}
{c |}      Mean
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res}-.2523782
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
      followup_c~d
Mean {res}   -.25237822
{reset}
{ralign 12:Variable} {...}
{c |}      Mean
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res} .1361831
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
      followup_c~d
Mean {res}    .13618309
{reset}
{ralign 12:Variable} {...}
{c |}        SD
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res}  1.09432
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
    followup_c~d
SD {res}    1.0943199
{reset}
{ralign 12:Variable} {...}
{c |}        SD
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res} .8725076
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
    followup_c~d
SD {res}    .87250763
{reset}
{com}. 
. matrix n_cog = J(1,1,.)
{txt}
{com}. forvalues i = 1/1 {c -(}
{txt}  2{com}.         matrix n_cog[1,`i'] = n_tr_cog[1,`i'] + n_ct_cog[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}196
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 32
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}6.1
{col 69}{txt}max{col 72} = {res} 12
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.3885613{col 38}{space 1}  -2.13{col 46}{space 3}0.034{col 54}{space 3}-.8055191{col 66}{space 3}-.0245301
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}.         
. matrix r2_followup_cog_std_temp = r(table)
{txt}
{com}. 
. 
. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix r2_followup_cog_std_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix r2_followup_cog_std_mean[1,`j'] = r2_followup_cog_std_temp[1,`j']
{txt}  3{com}. * standard error
. * matrix r2_followup_cog_std_se[1,`j'] = r2_followup_cog_std_temp[2,`j']
. * p value
. matrix r2_followup_cog_std_pv[1,`j'] = r2_followup_cog_std_temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}.     
. /// Non cognitive
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat followup_noncog_std RSES_std CPCS_std if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_noncog = r(StatTotal)
{txt}  5{com}. 
. tabstat followup_noncog_std RSES_std CPCS_std if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_noncog = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}       67        94        94
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   followup_n~d      RSES_std      CPCS_std
N {res}           67            94            94
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}       74        96        96
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   followup_n~d      RSES_std      CPCS_std
N {res}           74            96            96
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .4791484  .3486604  .3437962
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      followup_n~d      RSES_std      CPCS_std
Mean {res}    .47914836     .34866036     .34379623
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res}-.2794302 -.2320565  -.254617
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      followup_n~d      RSES_std      CPCS_std
Mean {res}   -.27943024    -.23205648    -.25461705
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} .9127414  1.028993  1.019982
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    followup_n~d      RSES_std      CPCS_std
SD {res}    .91274144     1.0289934     1.0199822
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} .9279901  .9228443  .9357831
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    followup_n~d      RSES_std      CPCS_std
SD {res}    .92799012     .92284427     .93578307
{reset}
{com}. 
. matrix n_noncog = J(1,3,.)
{txt}
{com}. forvalues i = 1/3 {c -(}
{txt}  2{com}.         matrix n_noncog[1,`i'] = n_tr_noncog[1,`i'] + n_ct_noncog[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in followup_noncog_std RSES_std CPCS_std{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}141
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 30
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}4.7
{col 69}{txt}max{col 72} = {res} 11
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}     followup_noncog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .7585786{col 38}{space 1}   3.71{col 46}{space 3}0.004{col 54}{space 3} .2887899{col 66}{space 3} 1.172379
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}190
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 32
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}5.9
{col 69}{txt}max{col 72} = {res} 12
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .5807168{col 38}{space 1}   2.67{col 46}{space 3}0.024{col 54}{space 3} .0814005{col 66}{space 3} 1.030542
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}190
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 32
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}5.9
{col 69}{txt}max{col 72} = {res} 12
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .5984133{col 38}{space 1}   2.74{col 46}{space 3}0.014{col 54}{space 3} .1330404{col 66}{space 3} 1.056814
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. 
. /// Behavioral
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat hyper if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_hyper = r(StatTotal)
{txt}  5{com}. 
. tabstat hyper if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_hyper = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}{ralign 12:Variable} {...}
{c |}         N
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res}       77
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
   hyper
N {res}    77
{reset}
{ralign 12:Variable} {...}
{c |}         N
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res}       71
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
   hyper
N {res}    71
{reset}
{ralign 12:Variable} {...}
{c |}      Mean
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res} .2597403
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
          hyper
Mean {res} .25974026
{reset}
{ralign 12:Variable} {...}
{c |}      Mean
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res} .0704225
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
          hyper
Mean {res} .07042254
{reset}
{ralign 12:Variable} {...}
{c |}        SD
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res} .4413674
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
        hyper
SD {res} .44136741
{reset}
{ralign 12:Variable} {...}
{c |}        SD
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res} .2576789
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
        hyper
SD {res} .25767885
{reset}
{com}. 
. matrix n_hyper = J(1,1,.)
{txt}
{com}. forvalues i = 1/1 {c -(}
{txt}  2{com}.         matrix n_hyper[1,`i'] = n_tr_hyper[1,`i'] + n_ct_hyper[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in hyper{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment if hypernoinfo == 0, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}148
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 31
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}4.8
{col 69}{txt}max{col 72} = {res} 12
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                   hyper{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1893177{col 38}{space 1}   2.95{col 46}{space 3}0.014{col 54}{space 3} .0487644{col 66}{space 3} .3380837
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. 
. // significant level
. 
. local outcome DT_score_pre_std rosen_pre_std cpcs_pre_std hhmember hhheadage hhheadeduyear q2a q2c q2h tutor study_other followup_cog_std RSES_std CPCS_std
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}.                 if r2_`dep'_pv[1,1]<=0.01 {c -(}
{txt}  3{com}.                         local star_`dep' %3s "***"
{txt}  4{com}.                 {c )-}
{txt}  5{com}.                 else if (r2_`dep'_pv[1,1]>0.01) & (r2_`dep'_pv[1,1]<=0.05) {c -(}
{txt}  6{com}.                         local star_`dep' %2s "**"
{txt}  7{com}.                 {c )-}
{txt}  8{com}.                 else if (r2_`dep'_pv[1,1]>0.05) & (r2_`dep'_pv[1,1]<=0.10) {c -(}
{txt}  9{com}.                         local star_`dep' %1s "*"
{txt} 10{com}.                 {c )-}
{txt} 11{com}.                 else {c -(}
{txt} 12{com}.                         local star_`dep'  ""
{txt} 13{com}.                 {c )-}
{txt} 14{com}. {c )-} 
{txt}
{com}. 
. rwolf DT_score_pre_std rosen_pre_std cpcs_pre_std hhmember hhheadage hhheadeduyear, indepvar(treatment) reps(1000)
Bootstrap replications (1000). This may take some time.
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Romano-Wolf step-down adjusted p-values


Independent variable:  treatment
Outcome variables:   DT_score_pre_std rosen_pre_std cpcs_pre_std hhmember
{col 22}hhheadage hhheadeduyear
Number of resamples: 1000


{hline 78}
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
{hline 20}+{hline 57}
   {txt}DT_score_pre_std {c |}    {res}0.3161             0.3447              0.8312
      {txt}rosen_pre_std {c |}    {res}0.7183             0.7003              0.9211
       {txt}cpcs_pre_std {c |}    {res}0.1937             0.1848              0.6763
           {txt}hhmember {c |}    {res}0.5968             0.5694              0.9211
          {txt}hhheadage {c |}    {res}0.6358             0.6533              0.9211
      {txt}hhheadeduyear {c |}    {res}0.3081             0.3127              0.8312
{hline 78}
{txt}
{com}. rwolf q2a q2c q2h tutor study_other followup_cog_std RSES_std CPCS_std, indepvar(treatment) reps(1000)
Bootstrap replications (1000). This may take some time.
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Romano-Wolf step-down adjusted p-values


Independent variable:  treatment
Outcome variables:   q2a q2c q2h tutor study_other followup_cog_std RSES_std CPCS_std
Number of resamples: 1000


{hline 78}
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
{hline 20}+{hline 57}
                {txt}q2a {c |}    {res}0.5680             0.5624              0.8731
                {txt}q2c {c |}    {res}0.7345             0.7602              0.8731
                {txt}q2h {c |}    {res}0.1919             0.1958              0.5524
              {txt}tutor {c |}    {res}0.1936             0.1828              0.5524
        {txt}study_other {c |}    {res}0.5680             0.5584              0.8731
   {txt}followup_cog_std {c |}    {res}0.0066             0.0130              0.0360
           {txt}RSES_std {c |}    {res}0.0001             0.0020              0.0020
           {txt}CPCS_std {c |}    {res}0.0000             0.0010              0.0010
{hline 78}
{txt}
{com}. 
. 
. 
. 
. /// Table
> tempname hh2
{txt}
{com}. file open `hh2' using "$path_output/summary_stat_psmatch.tex", write replace
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "% Author: Kazuma Takakura" _newline
{txt}
{com}. file write `hh2' "% Date: `c(current_date)'" _newline
{txt}
{com}. file write `hh2' "% Time: `c(current_time)'" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. 
. file write `hh2' "\begin{c -(}table{c )-}[h!]\footnotesize" _newline
{txt}
{com}. file write `hh2' "  \centering" _newline
{txt}
{com}. file write `hh2' "  \caption{c -(}Summary Statistics (Matched Sample){c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:sumstat_psm{c )-}" _newline
{txt}
{com}. file write `hh2' "\scalebox{c -(}1{c )-}{c -(}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}threeparttable{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "\begin{c -(}tabular{c )-}{c -(}lccccc{c )-}\toprule" _newline
{txt}
{com}. 
.   
. file write `hh2' " Dependent Variable & Treatment &  Control  & Difference & N   \\\midrule\midrule" _newline
{txt}
{com}. file write `hh2' " Panel A: Baseline & & & &   \\ " _newline
{txt}
{com}. file write `hh2' " DT score^{c -(}a{c )-} & " %04.3f (mean_tr_bl[1,1]) " & " %04.3f (mean_ct_bl[1,1]) " & " %04.3f (r2_DT_score_pre_std_mean[1,1]) `star_DT_score_pre_std' " & " (n_bl[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_bl[1,1]) " ] & [ " %04.3f (sd_ct_bl[1,1]) " ] & ( " %04.3f (r2_DT_score_pre_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.893) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. 
. file write `hh2' " RSES score^{c -(}a{c )-} & " %04.3f (mean_tr_bl[1,2]) " & " %04.3f (mean_ct_bl[1,2]) " & " %04.3f (r2_rosen_pre_std_mean[1,1]) `star_rosen_pre_std' " & "  (n_bl[1,2]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_bl[1,2]) " ] & [ " %04.3f (sd_ct_bl[1,2]) " ] & ( " %04.3f (r2_rosen_pre_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.948) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " CPCS score^{c -(}a{c )-} & " %04.3f (mean_tr_bl[1,3]) " & " %04.3f (mean_ct_bl[1,3]) " & " %04.3f (r2_cpcs_pre_std_mean[1,1]) `star_cpcs_pre_std' " & "  (n_bl[1,3]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_bl[1,3]) " ] & [ " %04.3f (sd_ct_bl[1,3]) " ] & ( " %04.3f (r2_cpcs_pre_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.806) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Household size & " %04.3f (mean_tr_parent[1,1]) " & " %04.3f (mean_ct_parent[1,1]) " & " %04.3f (r2_hhmember_mean[1,1]) `star_hhmember'  " & " (n_parent[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_parent[1,1]) " ] & [ " %04.3f (sd_ct_parent[1,1]) " ] & ( " %04.3f (r2_hhmember_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.948) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Household head age & " %04.3f (mean_tr_parent[1,2]) " & " %04.3f (mean_ct_parent[1,2]) " & " %04.3f (r2_hhheadage_mean[1,1]) `star_hhheadage' " & "  (n_parent[1,2]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_parent[1,2]) " ] & [ " %04.3f (sd_ct_parent[1,2]) " ] & ( " %04.3f (r2_hhheadage_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.948) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Household head education & " %04.3f (mean_tr_parent[1,3]) " & " %04.3f (mean_ct_parent[1,3]) " & " %04.3f (r2_hhheadeduyear_mean[1,1]) `star_hhheadeduyear' " & "  (n_parent[1,3]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_parent[1,3]) " ] & [ " %04.3f (sd_ct_parent[1,3]) " ] & ( " %04.3f (r2_hhheadeduyear_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.893) " \{c )-} &   \\ " _newline
{txt}
{com}. file write `hh2' " \\ "_newline
{txt}
{com}. 
. file write `hh2' " Panel B: Follow-up & & & &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " School attendance & " %04.3f (mean_tr_school[1,1]) " & " %04.3f (mean_ct_school[1,1]) " & " %04.3f (r2_q2a_mean[1,1]) `star_q2a' " & " (n_school[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_school[1,1]) " ] & [ " %04.3f (sd_ct_school[1,1]) " ] & ( " %04.3f (r2_q2a_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.818) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Grade repeat & " %04.3f (mean_tr_school[1,3]) " & " %04.3f (mean_ct_school[1,3]) " & " %04.3f (r2_q2c_mean[1,1]) `star_q2c' " & "  (n_school[1,3]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_school[1,3]) " ] & [ " %04.3f (sd_ct_school[1,3]) " ] & ( " %04.3f (r2_q2c_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.818) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Drop out & " %04.3f (mean_tr_school[1,4]) " & " %04.3f (mean_ct_school[1,4]) " & " %04.3f (r2_q2h_mean[1,1]) `star_q2h'  " & "  (n_school[1,4]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_school[1,4]) " ] & [ " %04.3f (sd_ct_school[1,4]) " ] & ( " %04.3f (r2_q2h_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.637) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Tutoring & " %04.3f (mean_tr_study[1,1]) " & " %04.3f (mean_ct_study[1,1]) " & " %04.3f (r2_tutor_mean[1,1]) `star_tutor'  " & " (n_study[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_study[1,1]) " ] & [ " %04.3f (sd_ct_study[1,1]) " ] & ( " %04.3f (r2_tutor_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.637) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Self-study & " %04.3f (mean_tr_study[1,2]) " & " %04.3f (mean_ct_study[1,2]) " & " %04.3f (r2_study_other_mean[1,1]) `star_study_other' " & "  (n_study[1,2]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_study[1,2]) " ] & [ " %04.3f (sd_ct_study[1,2]) " ] & ( " %04.3f (r2_study_other_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.811) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Rapid math test score^{c -(}a{c )-} & " %04.3f (mean_tr_cog[1,1]) " & " %04.3f (mean_ct_cog[1,1]) " & " %04.3f (r2_followup_cog_std_mean[1,1]) `star_followup_cog_std'  "  & "  (n_cog[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_cog[1,1]) " ] & [ " %04.3f (sd_ct_cog[1,1]) " ] & ( " %04.3f (r2_followup_cog_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.203) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " RSES score^{c -(}a{c )-} & " %04.3f (mean_tr_noncog[1,2]) " & " %04.3f (mean_ct_noncog[1,2]) " & " %04.3f (r2_RSES_std_mean[1,1])   `star_RSES_std' " & " (n_noncog[1,2]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_noncog[1,2]) " ] & [ " %04.3f (sd_ct_noncog[1,2]) " ] & ( " %04.3f (r2_RSES_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.002) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " CPCS score^{c -(}a{c )-} & " %04.3f (mean_tr_noncog[1,3]) " & " %04.3f (mean_ct_noncog[1,3]) " & " %04.3f (r2_CPCS_std_mean[1,1])   `star_CPCS_std' "&  " (n_noncog[1,3]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_noncog[1,3]) " ] & [ " %04.3f (sd_ct_noncog[1,3]) " ] & ( " %04.3f (r2_CPCS_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.001) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. 
. 
. 
. file write `hh2' "\midrule" _newline
{txt}
{com}. file write `hh2' "\end{c -(}tabular{c )-}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\item (a) Standardized in the follow-up sample. " _newline
{txt}
{com}. file write `hh2' "\item (b) For the propensity score matching, we use covariates including student's grade, sex, branch dummy (location), parents' income source, last income per family member, number of household members, age of household head, education level of household head, teacher's age, sex, and phone survey dummy." _newline
{txt}
{com}. file write `hh2' "\item (c) Standard deviations are reported in square brackets. Standard errors, which are reported within parentheses are clustered by school at the baseline. " _newline
{txt}
{com}. file write `hh2' "\item (d) Romano-Wolf multiple hypothesis testing p-values are reported in curly brackets." _newline
{txt}
{com}. file write `hh2' "Statistical significance is indicated by stars based on the wild clustered bootstrap p-values reported in parentheses: $*$ denotes significance at the 10\% level, $∗∗$ at the 5\% level, and $∗∗∗$ at the 1\% level. " _newline
{txt}
{com}. file write `hh2' "\end{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}threeparttable{c )-}" _newline
{txt}
{com}. file write `hh2' "{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:addlabel{c )-}%" _newline
{txt}
{com}. file write `hh2' "\end{c -(}table{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. file close `hh2'
{txt}
{com}. 
{txt}end of do-file

{com}. 
. do "$path_do/3_table_C1.do"
{txt}
{com}. * This is the do file to create "Table C1. Heterogeneity across Baseline Abilites (Quantile)"
. set seed 123
{txt}
{com}. 
. use "$path_data/temp/followup_student_parents_matched", clear
{txt}
{com}. 
. 
. xtile DT_score_pre_std_quartile = DT_score_pre_std , nq(4)
{txt}
{com}. gen DT_score_pre_std_q1 = 1 if DT_score_pre_std_quartile == 1
{txt}(183 missing values generated)

{com}. gen DT_score_pre_std_q2 = 1 if DT_score_pre_std_quartile == 2
{txt}(182 missing values generated)

{com}. gen DT_score_pre_std_q3 = 1 if DT_score_pre_std_quartile == 3
{txt}(183 missing values generated)

{com}. gen DT_score_pre_std_q4 = 1 if DT_score_pre_std_quartile == 4
{txt}(185 missing values generated)

{com}. recode DT_score_pre_std_q* (.=0)
{txt}(4 changes made to {bf:DT_score_pre_std_quartile})
(183 changes made to {bf:DT_score_pre_std_q1})
(182 changes made to {bf:DT_score_pre_std_q2})
(183 changes made to {bf:DT_score_pre_std_q3})
(185 changes made to {bf:DT_score_pre_std_q4})

{com}. 
. xtile rosen_pre_std_quartile = rosen_pre_std , nq(4)
{txt}
{com}. gen rosen_pre_std_q1 = 1 if rosen_pre_std_quartile == 1
{txt}(182 missing values generated)

{com}. gen rosen_pre_std_q2 = 1 if rosen_pre_std_quartile == 2
{txt}(176 missing values generated)

{com}. gen rosen_pre_std_q3 = 1 if rosen_pre_std_quartile == 3
{txt}(179 missing values generated)

{com}. gen rosen_pre_std_q4 = 1 if rosen_pre_std_quartile == 4
{txt}(192 missing values generated)

{com}. recode rosen_pre_std_q* (.=0)
{txt}(0 changes made to {bf:rosen_pre_std_quartile})
(182 changes made to {bf:rosen_pre_std_q1})
(176 changes made to {bf:rosen_pre_std_q2})
(179 changes made to {bf:rosen_pre_std_q3})
(192 changes made to {bf:rosen_pre_std_q4})

{com}. 
. xtile cpcs_pre_std_quartile = cpcs_pre_std , nq(4)
{txt}
{com}. gen cpcs_pre_std_q1 = 1 if cpcs_pre_std_quartile == 1
{txt}(182 missing values generated)

{com}. gen cpcs_pre_std_q2 = 1 if cpcs_pre_std_quartile == 2
{txt}(169 missing values generated)

{com}. gen cpcs_pre_std_q3 = 1 if cpcs_pre_std_quartile == 3
{txt}(190 missing values generated)

{com}. gen cpcs_pre_std_q4 = 1 if cpcs_pre_std_quartile == 4
{txt}(188 missing values generated)

{com}. recode cpcs_pre_std_q* (.=0)
{txt}(0 changes made to {bf:cpcs_pre_std_quartile})
(182 changes made to {bf:cpcs_pre_std_q1})
(169 changes made to {bf:cpcs_pre_std_q2})
(190 changes made to {bf:cpcs_pre_std_q3})
(188 changes made to {bf:cpcs_pre_std_q4})

{com}. 
. 
. /// Cognitive
> wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_q1 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 60
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 26
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  5
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.5077876{col 38}{space 1}  -1.61{col 46}{space 3}0.148{col 54}{space 3}-1.187057{col 66}{space 3} .1533403
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_q1 = e(N)
{txt}
{com}. scalar n_clust_cog_q1 = e(N_clust)
{txt}
{com}. matrix cog_q1 = r(table)
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_q2 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:19})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 61
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 27
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  5
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2037149{col 38}{space 1}   0.77{col 46}{space 3}0.466{col 54}{space 3}-.3472831{col 66}{space 3} .8129373
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_q2 = e(N)
{txt}
{com}. scalar n_clust_cog_q2 = e(N_clust)
{txt}
{com}. matrix cog_q2 = r(table)
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_q3 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 60
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  6
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.3082515{col 38}{space 1}  -0.95{col 46}{space 3}0.374{col 54}{space 3}-1.021192{col 66}{space 3}  .510421
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_q3 = e(N)
{txt}
{com}. scalar n_clust_cog_q3 = e(N_clust)
{txt}
{com}. matrix cog_q3 = r(table)
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_q4 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 58
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 20
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.9
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} -.193173{col 38}{space 1}  -0.66{col 46}{space 3}0.520{col 54}{space 3}-.8850385{col 66}{space 3} .3878688
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_q4 = e(N)
{txt}
{com}. scalar n_clust_cog_q4 = e(N_clust)
{txt}
{com}. matrix cog_q4 = r(table)
{txt}
{com}. 
. /// RSES
> wildbootstrap reg RSES_std treatment if rosen_pre_std_q1 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 59
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 25
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.4
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2813655{col 38}{space 1}   1.04{col 46}{space 3}0.290{col 54}{space 3}-.2814048{col 66}{space 3} .8876854
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_q1 = e(N)
{txt}
{com}. scalar n_clust_rses_q1 = e(N_clust)
{txt}
{com}. matrix rses_q1 = r(table)
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if rosen_pre_std_q2 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 27
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.4
{col 69}{txt}max{col 72} = {res}  6
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0636143{col 38}{space 1}  -0.24{col 46}{space 3}0.768{col 54}{space 3}-.6439179{col 66}{space 3} .4751346
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_q2 = e(N)
{txt}
{com}. scalar n_clust_rses_q2 = e(N_clust)
{txt}
{com}. matrix rses_q2 = r(table)
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if rosen_pre_std_q3 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:29})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 62
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .5762421{col 38}{space 1}   1.87{col 46}{space 3}0.078{col 54}{space 3}-.0656301{col 66}{space 3} 1.198381
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_q3 = e(N)
{txt}
{com}. scalar n_clust_rses_q3 = e(N_clust)
{txt}
{com}. matrix rses_q3 = r(table)
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if rosen_pre_std_q4 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 50
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.1
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} 1.014172{col 38}{space 1}   3.01{col 46}{space 3}0.008{col 54}{space 3} .3209694{col 66}{space 3} 1.708612
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_q4 = e(N)
{txt}
{com}. scalar n_clust_rses_q4 = e(N_clust)
{txt}
{com}. matrix rses_q4 = r(table)
{txt}
{com}. 
. /// CPCS
> wildbootstrap reg CPCS_std treatment if cpcs_pre_std_q1 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 59
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.5
{col 69}{txt}max{col 72} = {res}  6
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .4397484{col 38}{space 1}   1.72{col 46}{space 3}0.116{col 54}{space 3}-.1243137{col 66}{space 3} 1.063891
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_q1 = e(N)
{txt}
{com}. scalar n_clust_cpcs_q1 = e(N_clust)
{txt}
{com}. matrix cpcs_q1 = r(table)
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if cpcs_pre_std_q2 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 72
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 26
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  6
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2661398{col 38}{space 1}   0.82{col 46}{space 3}0.404{col 54}{space 3}-.4102651{col 66}{space 3} .9510999
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_q2 = e(N)
{txt}
{com}. scalar n_clust_cpcs_q2 = e(N_clust)
{txt}
{com}. matrix cpcs_q2 = r(table)
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if cpcs_pre_std_q3 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 52
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 26
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.0
{col 69}{txt}max{col 72} = {res}  4
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .6674939{col 38}{space 1}   1.82{col 46}{space 3}0.148{col 54}{space 3}-.2919796{col 66}{space 3} 1.377259
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_q3 = e(N)
{txt}
{com}. scalar n_clust_cpcs_q3 = e(N_clust)
{txt}
{com}. matrix cpcs_q3 = r(table)
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if cpcs_pre_std_q4 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:19})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}   .58559{col 38}{space 1}   2.14{col 46}{space 3}0.040{col 54}{space 3} .0522024{col 66}{space 3} 1.273326
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_q4 = e(N)
{txt}
{com}. scalar n_clust_cpcs_q4 = e(N_clust)
{txt}
{com}. matrix cpcs_q4 = r(table)
{txt}
{com}. 
. // results
. foreach i in cog rses cpcs{c -(}
{txt}  2{com}. foreach j in 1 2 3 4 {c -(}
{txt}  3{com}. * beta
. scalar `i'_q`j'_est = `i'_q`j'[1,1]
{txt}  4{com}. * standard error
. * scalar `i'_q`j'_se = `i'_q`j'[2,1]
. * p value
. scalar `i'_q`j'_pv = `i'_q`j'[3,1]
{txt}  5{com}. 
. // significant level
. if `i'_q`j'_pv <= 0.01 {c -(}
{txt}  6{com}. local star_`i'_q`j' %3s "***"
{txt}  7{com}. {c )-}
{txt}  8{com}. else if (`i'_q`j'_pv >0.01) & (`i'_q`j'_pv <=0.05) {c -(}
{txt}  9{com}. local star_`i'_q`j' %2s "**"
{txt} 10{com}. {c )-}
{txt} 11{com}. else if (`i'_q`j'_pv >0.05) & (`i'_q`j'_pv <=0.10) {c -(}
{txt} 12{com}. local star_`i'_q`j' %1s "*"
{txt} 13{com}. {c )-}
{txt} 14{com}. else {c -(}
{txt} 15{com}. local star_`i'_q`j'  ""
{txt} 16{com}. {c )-}
{txt} 17{com}. {c )-}
{txt} 18{com}. {c )-}
{txt}
{com}. 
. 
. 
. 
. 
. /// Table
> tempname hh2
{txt}
{com}. file open `hh2' using "$path_output/hetero_quartile.tex", write replace
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "% Author: Kazuma Takakura" _newline
{txt}
{com}. file write `hh2' "% Date: `c(current_date)'" _newline
{txt}
{com}. file write `hh2' "% Time: `c(current_time)'" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. 
. file write `hh2' "\begin{c -(}table{c )-}[h!]\footnotesize" _newline
{txt}
{com}. file write `hh2' "  \centering" _newline
{txt}
{com}. file write `hh2' "  \caption{c -(}Heterogeneity among Baseline Abilites (Quantile){c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:hetero_quantile{c )-}" _newline
{txt}
{com}. file write `hh2' "\scalebox{c -(}1{c )-}{c -(}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}threeparttable{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "\begin{c -(}tabular{c )-}{c -(}lccccccc{c )-}\toprule" _newline
{txt}
{com}. 
.   
. file write `hh2' " Dependent Variable & Baseline  & Difference & Obs & N of clusters  \\\midrule\midrule" _newline
{txt}
{com}. file write `hh2' " Rapid math test score^{c -(}a{c )-} & Math quartile 4  & " %04.3f (cog_q4_est) `star_cog_q4' " & " %02.0f (n_cog_q4) " & " %02.0f ( n_clust_cog_q4 ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &        & ( " %04.3f (cog_q4_pv) " ) & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & Math quartile 3  & " %04.3f (cog_q3_est) `star_cog_q3' " & " %02.0f (n_cog_q3) " & " %02.0f ( n_clust_cog_q3 ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &        & ( " %04.3f (cog_q3_pv) " ) & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & Math quartile 2  & " %04.3f (cog_q2_est) `star_cog_q2' " & " %02.0f (n_cog_q2) " & " %02.0f ( n_clust_cog_q2 ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &        & ( " %04.3f (cog_q2_pv) " ) & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & Math quartile 1  & " %04.3f (cog_q1_est) `star_cog_q1' " & " %02.0f (n_cog_q1) " & " %02.0f ( n_clust_cog_q1 ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &        & ( " %04.3f (cog_q1_pv) " ) & &  \\ " _newline
{txt}
{com}. 
. file write `hh2' " RSES^{c -(}a{c )-} & RSES quartile 4  & " %04.3f (rses_q4_est) `star_rses_q4' " & " %02.0f (n_rses_q4) " & " %02.0f ( n_clust_rses_q4 ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &        & ( " %04.3f (rses_q4_pv) " ) & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & RSES quartile 3  & " %04.3f (rses_q3_est) `star_rses_q3' " & " %02.0f (n_rses_q3) " & " %02.0f ( n_clust_rses_q3 ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &        & ( " %04.3f (rses_q3_pv) " ) & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & RSES quartile 2  & " %04.3f (rses_q2_est) `star_rses_q2' " & " %02.0f (n_rses_q2) " & " %02.0f ( n_clust_rses_q2 ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &        & ( " %04.3f (rses_q2_pv) " ) & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & RSES quartile 1  & " %04.3f (rses_q1_est) `star_rses_q1' " & " %02.0f (n_rses_q1) " & " %02.0f ( n_clust_rses_q1 ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &        & ( " %04.3f (rses_q1_pv) " ) & &  \\ " _newline
{txt}
{com}. 
. file write `hh2' " CPCS^{c -(}a{c )-} & CPCS quartile 4  & " %04.3f (cpcs_q4_est) `star_cpcs_q4' " & " %02.0f (n_cpcs_q4) " & " %02.0f ( n_clust_cpcs_q4 ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &        & ( " %04.3f (cpcs_q4_pv) " ) & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & CPCS quartile 3  & " %04.3f (cpcs_q3_est) `star_cpcs_q3' " & " %02.0f (n_cpcs_q3) " & " %02.0f ( n_clust_cpcs_q3 ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &        & ( " %04.3f (cpcs_q3_pv) " ) & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & CPCS quartile 2  & " %04.3f (cpcs_q2_est) `star_cpcs_q2' " & " %02.0f (n_cpcs_q2) " & " %02.0f ( n_clust_cpcs_q2 ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &        & ( " %04.3f (cpcs_q2_pv) " ) & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & CPCS quartile 1  & " %04.3f (cpcs_q1_est) `star_cpcs_q1' " & " %02.0f (n_cpcs_q1) " & " %02.0f ( n_clust_cpcs_q1 ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &        & ( " %04.3f (cpcs_q1_pv) " ) & &  \\ " _newline
{txt}
{com}. 
. file write `hh2' "\\\midrule" _newline
{txt}
{com}. file write `hh2' "\end{c -(}tabular{c )-}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\item (a) Dependent variables are standardized using the average and variance of the whole follow-up sample in the February 2022 survey. " _newline
{txt}
{com}. file write `hh2' "\item (b) Cutoffs are created based on their ability to perform each item at the baseline. " _newline
{txt}
{com}. file write `hh2' "\item (c) Wild cluster bootstrap p-values are reported within parentheses. Clusters are schools at the baseline." _newline
{txt}
{com}. file write `hh2' "\item (d) $^*$ Significant at 10\% level; $^{c -(}**{c )-}$ significant at 5\% level; $^{c -(}***{c )-}$ significant at 1\% level. " _newline
{txt}
{com}. file write `hh2' "\end{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}threeparttable{c )-}" _newline
{txt}
{com}. file write `hh2' "{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:addlabel{c )-}%" _newline
{txt}
{com}. file write `hh2' "\end{c -(}table{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. file close `hh2'
{txt}
{com}. 
{txt}end of do-file

{com}. 
. do "$path_do/3_table_C2.do"
{txt}
{com}. * This is the do file to create "Table C2. Heterogeneity across Baseline Abilites (Math and RSES)"
. set seed 123
{txt}
{com}. 
. use "$path_data/temp/followup_student_baseline_score_missing_dummy", replace
{txt}
{com}. 
. egen DT_score_pre_std_med = median(DT_score_pre_std)
{txt}
{com}. gen DT_score_pre_std_upper50 = 1 if DT_score_pre_std>DT_score_pre_std_med
{txt}(121 missing values generated)

{com}. recode DT_score_pre_std_upper50 (.=0)
{txt}(121 changes made to {bf:DT_score_pre_std_upper50})

{com}. 
. egen rosen_pre_std_med = median(rosen_pre_std)
{txt}
{com}. gen rosen_pre_std_upper50 = 1 if rosen_pre_std>rosen_pre_std_med
{txt}(128 missing values generated)

{com}. recode rosen_pre_std_upper50 (.=0)
{txt}(128 changes made to {bf:rosen_pre_std_upper50})

{com}. rename rosen_pre_std_upper50 RSES_std_upper50
{res}{txt}
{com}. 
. egen cpcs_pre_std_med = median(cpcs_pre_std)
{txt}
{com}. gen cpcs_pre_std_upper50 = 1 if cpcs_pre_std>cpcs_pre_std_med
{txt}(135 missing values generated)

{com}. recode cpcs_pre_std_upper50 (.=0)
{txt}(135 changes made to {bf:cpcs_pre_std_upper50})

{com}. rename cpcs_pre_std_upper50 CPCS_std_upper50
{res}{txt}
{com}. 
. 
. /// Cognitive
> wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 59
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 22
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2397567{col 38}{space 1}  -0.86{col 46}{space 3}0.444{col 54}{space 3} -.835023{col 66}{space 3} .4309843
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_rses_u = e(N)
{txt}
{com}. scalar n_clust_cog_u_rses_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.3459862{col 38}{space 1}  -1.14{col 46}{space 3}0.246{col 54}{space 3}-.9646946{col 66}{space 3} .2967857
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_rses_l = e(N)
{txt}
{com}. scalar n_clust_cog_u_rses_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2419353{col 38}{space 1}  -0.87{col 46}{space 3}0.390{col 54}{space 3}-.8433267{col 66}{space 3} .3920268
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_rses_u = e(N)
{txt}
{com}. scalar n_clust_cog_l_rses_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0781293{col 38}{space 1}  -0.29{col 46}{space 3}0.794{col 54}{space 3}-.6038382{col 66}{space 3} .5443816
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_rses_l = e(N)
{txt}
{com}. scalar n_clust_cog_l_rses_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 55
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0533319{col 38}{space 1}   0.17{col 46}{space 3}0.872{col 54}{space 3}-.6242397{col 66}{space 3} .7907538
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_cpcs_u = e(N)
{txt}
{com}. scalar n_clust_cog_u_cpcs_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 67
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.5660393{col 38}{space 1}  -1.84{col 46}{space 3}0.092{col 54}{space 3}-1.245036{col 66}{space 3} .1419984
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_cpcs_l = e(N)
{txt}
{com}. scalar n_clust_cog_u_cpcs_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0295186{col 38}{space 1}   0.10{col 46}{space 3}0.942{col 54}{space 3}-.6165229{col 66}{space 3} .6514795
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_cpcs_u = e(N)
{txt}
{com}. scalar n_clust_cog_l_cpcs_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 68
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  -.19243{col 38}{space 1}  -0.73{col 46}{space 3}0.480{col 54}{space 3}-.7462179{col 66}{space 3} .3675341
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_cpcs_l = e(N)
{txt}
{com}. scalar n_clust_cog_l_cpcs_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. 
. 
. /// RSES
> wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .9579135{col 38}{space 1}   3.36{col 46}{space 3}0.000{col 54}{space 3} .3158411{col 66}{space 3} 1.651529
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_u_cog_u = e(N)
{txt}
{com}. scalar n_clust_rses_u_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 61
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0615952{col 38}{space 1}   0.32{col 46}{space 3}0.748{col 54}{space 3} -.335182{col 66}{space 3} .4398791
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_l_cog_u = e(N)
{txt}
{com}. scalar n_clust_rses_l_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:29})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .4920182{col 38}{space 1}   1.44{col 46}{space 3}0.152{col 54}{space 3}-.1956882{col 66}{space 3} 1.132075
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_u_cog_l = e(N)
{txt}
{com}. scalar n_clust_rses_u_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1388312{col 38}{space 1}   0.42{col 46}{space 3}0.748{col 54}{space 3}-.6586927{col 66}{space 3} .8819884
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_l_cog_l = e(N)
{txt}
{com}. scalar n_clust_rses_l_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 52
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 20
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .6664341{col 38}{space 1}   3.26{col 46}{space 3}0.004{col 54}{space 3} .2256924{col 66}{space 3}  1.16988
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3847293{col 38}{space 1}   1.53{col 46}{space 3}0.150{col 54}{space 3}-.1439999{col 66}{space 3} .9723153
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2062219{col 38}{space 1}   0.47{col 46}{space 3}0.630{col 54}{space 3}-.9392839{col 66}{space 3} 1.108811
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 66
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3332931{col 38}{space 1}   0.96{col 46}{space 3}0.416{col 54}{space 3}-.4864367{col 66}{space 3} 1.101715
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. 
. 
. /// CPCS
> 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} 1.020132{col 38}{space 1}   3.66{col 46}{space 3}0.002{col 54}{space 3} .4199485{col 66}{space 3} 1.782028
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 61
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0665639{col 38}{space 1}   0.34{col 46}{space 3}0.706{col 54}{space 3}-.3338298{col 66}{space 3} .4994486
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .5458399{col 38}{space 1}   1.63{col 46}{space 3}0.112{col 54}{space 3}-.2035501{col 66}{space 3} 1.209728
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1431476{col 38}{space 1}   0.41{col 46}{space 3}0.700{col 54}{space 3}-.6565782{col 66}{space 3} 1.032317
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:19})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 52
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 20
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  .774167{col 38}{space 1}   3.68{col 46}{space 3}0.006{col 54}{space 3}  .320576{col 66}{space 3} 1.297191
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_u_cog_u = e(N)
{txt}
{com}. scalar n_clust_cpcs_u_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3575321{col 38}{space 1}   1.51{col 46}{space 3}0.154{col 54}{space 3} -.150128{col 66}{space 3} .9146968
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_l_cog_u = e(N)
{txt}
{com}. scalar n_clust_cpcs_l_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2792052{col 38}{space 1}   0.63{col 46}{space 3}0.546{col 54}{space 3}-.9224898{col 66}{space 3} 1.348458
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_u_cog_l = e(N)
{txt}
{com}. scalar n_clust_cpcs_u_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 66
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}   .33148{col 38}{space 1}   0.95{col 46}{space 3}0.420{col 54}{space 3}-.5052153{col 66}{space 3} 1.053721
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_l_cog_l = e(N)
{txt}
{com}. scalar n_clust_cpcs_l_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. // significant level
. 
. 
. local outcome cog rses cpcs
{txt}
{com}. local hetero1 cogU cogL
{txt}
{com}. local hetero2 rsesU rsesL
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. foreach h1 in `hetero1'{c -(}
{txt}  3{com}. foreach h2 in `hetero2'{c -(}
{txt}  4{com}.                 if `dep'_`h1'_`h2'_pv[1,1]<=0.01 {c -(}
{txt}  5{com}.                         local star_`dep'_`h1'_`h2' %3s "***"
{txt}  6{com}.                 {c )-}
{txt}  7{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.01) & (`dep'_`h1'_`h2'_pv[1,1]<=0.05) {c -(}
{txt}  8{com}.                         local star_`dep'_`h1'_`h2' %2s "**"
{txt}  9{com}.                 {c )-}
{txt} 10{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.05) & (`dep'_`h1'_`h2'_pv[1,1]<=0.10) {c -(}
{txt} 11{com}.                         local star_`dep'_`h1'_`h2' %1s "*"
{txt} 12{com}.                 {c )-}
{txt} 13{com}.                 else {c -(}
{txt} 14{com}.                         local star_`dep'_`h1'_`h2'  ""
{txt} 15{com}.                 {c )-}
{txt} 16{com}. {c )-} 
{txt} 17{com}. {c )-}
{txt} 18{com}. {c )-}
{txt}
{com}. 
. local outcome cog rses cpcs
{txt}
{com}. local hetero1 cogU cogL
{txt}
{com}. local hetero2 cpcsU cpcsL
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. foreach h1 in `hetero1'{c -(}
{txt}  3{com}. foreach h2 in `hetero2'{c -(}
{txt}  4{com}.                 if `dep'_`h1'_`h2'_pv[1,1]<=0.01 {c -(}
{txt}  5{com}.                         local star_`dep'_`h1'_`h2' %3s "***"
{txt}  6{com}.                 {c )-}
{txt}  7{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.01) & (`dep'_`h1'_`h2'_pv[1,1]<=0.05) {c -(}
{txt}  8{com}.                         local star_`dep'_`h1'_`h2' %2s "**"
{txt}  9{com}.                 {c )-}
{txt} 10{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.05) & (`dep'_`h1'_`h2'_pv[1,1]<=0.10) {c -(}
{txt} 11{com}.                         local star_`dep'_`h1'_`h2' %1s "*"
{txt} 12{com}.                 {c )-}
{txt} 13{com}.                 else {c -(}
{txt} 14{com}.                         local star_`dep'_`h1'_`h2'  ""
{txt} 15{com}.                 {c )-}
{txt} 16{com}. {c )-} 
{txt} 17{com}. {c )-}
{txt} 18{com}. {c )-}
{txt}
{com}. 
. /// Table
> 
. tempname hh2
{txt}
{com}. file open `hh2' using "$path_output/hetero_2by2_RSES.tex", write replace
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "% Author: Kazuma Takakura" _newline
{txt}
{com}. file write `hh2' "% Date: `c(current_date)'" _newline
{txt}
{com}. file write `hh2' "% Time: `c(current_time)'" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. 
. file write `hh2' "\begin{c -(}table{c )-}[h!]\footnotesize" _newline
{txt}
{com}. file write `hh2' "  \centering" _newline
{txt}
{com}. file write `hh2' "  \caption{c -(}Heterogeneity among Baseline Abilites (Math and RSES){c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:hetero_rses{c )-}" _newline
{txt}
{com}. file write `hh2' "\scalebox{c -(}1{c )-}{c -(}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}threeparttable{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "\begin{c -(}tabular{c )-}{c -(}lccccccc{c )-}\toprule" _newline
{txt}
{com}. 
.  
. 
.   
. file write `hh2' " Dependent Variable & \multicolumn{c -(}2{c )-}{c -(}c{c )-}{c -(}Baseline^{c -(}b{c )-}{c )-} & Difference & Obs & N of clusters \\\midrule\midrule" _newline
{txt}
{com}. file write `hh2' " Rapid math test score^{c -(}a{c )-} & Math Top 50\%  & RSES Top 50\% & " %04.3f (cog_cogU_rsesU_mean[1,1]) `star_cog_cogU_rsesU' " & " %02.0f ( n_cog_u_rses_u ) " & " %02.0f ( n_clust_cog_u_rses_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogU_rsesU_pv[1,1]) " ) & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & & RSES Bottom 50\% & " %04.3f (cog_cogU_rsesL_mean[1,1]) `star_cog_cogU_rsesL' " & " %02.0f ( n_cog_u_rses_l ) " & " %02.0f ( n_clust_cog_u_rses_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogU_rsesL_pv[1,1]) " ) & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & Math Bottom 50\%  & RSES Top 50\% & " %04.3f (cog_cogL_rsesU_mean[1,1]) `star_cog_cogL_rsesU' " & " %02.0f ( n_cog_l_rses_u ) " & " %02.0f ( n_clust_cog_l_rses_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogL_rsesU_pv[1,1]) " ) & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & & RSES Bottom 50\% & " %04.3f (cog_cogL_rsesL_mean[1,1]) `star_cog_cogL_rsesL' " & " %02.0f ( n_cog_l_rses_l ) " & " %02.0f ( n_clust_cog_l_rses_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogL_rsesL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. 
. file write `hh2' " RSES score^{c -(}a{c )-} & Math Top 50\%  & RSES Top 50\% & " %04.3f (rses_cogU_rsesU_mean[1,1]) `star_rses_cogU_rsesU' " & " %02.0f ( n_rses_u_cog_u ) " & " %02.0f ( n_clust_rses_u_cog_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (rses_cogU_rsesU_pv[1,1]) " ) & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & & RSES Bottom 50\% & " %04.3f (rses_cogU_rsesL_mean[1,1]) `star_rses_cogU_rsesL' " & " %02.0f ( n_rses_l_cog_u ) " & " %02.0f ( n_clust_rses_l_cog_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &       & & ( " %04.3f (rses_cogU_rsesL_pv[1,1]) " ) & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & Math Bottom 50\%  & RSES Top 50\% & " %04.3f (rses_cogL_rsesU_mean[1,1]) `star_rses_cogL_rsesU' " & " %02.0f ( n_rses_u_cog_l ) " & " %02.0f ( n_clust_rses_u_cog_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (rses_cogL_rsesU_pv[1,1]) " ) & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & & RSES Bottom 50\% & " %04.3f (rses_cogL_rsesL_mean[1,1]) `star_rses_cogL_rsesL' " & " %02.0f ( n_rses_l_cog_l ) " & " %02.0f ( n_clust_rses_l_cog_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &       & & ( " %04.3f (rses_cogL_rsesL_pv[1,1]) " ) & &  \\ " _newline
{txt}
{com}. 
. file write `hh2' "\\\midrule" _newline
{txt}
{com}. file write `hh2' "\end{c -(}tabular{c )-}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\item (a) Dependent variables are standardized using the average and variance of the whole follow-up sample in the February 2022 survey. " _newline
{txt}
{com}. file write `hh2' "\item (b) Cutoffs are created based on whether their ability to perform each item at the baseline was higher or lower than the median. " _newline
{txt}
{com}. file write `hh2' "\item (c) Wild cluster bootstrap p-values are reported within parentheses. Clusters are schools at the baseline." _newline
{txt}
{com}. file write `hh2' "\item (d) $^*$ Significant at 10\% level; $^{c -(}**{c )-}$ significant at 5\% level; $^{c -(}***{c )-}$ significant at 1\% level. " _newline
{txt}
{com}. file write `hh2' "\end{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}threeparttable{c )-}" _newline
{txt}
{com}. file write `hh2' "{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:addlabel{c )-}%" _newline
{txt}
{com}. file write `hh2' "\end{c -(}table{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. file close `hh2'
{txt}
{com}. 
{txt}end of do-file

{com}. 
. do "$path_do/3_table_C3.do"
{txt}
{com}. * This is the do file to create "Table C3. Heterogeneity among Baseline Abilites (Matched Sample)"
. set seed 123
{txt}
{com}. 
. use "$path_data/temp/followup_student_parents_matched", clear
{txt}
{com}. 
. 
. gen gend = q1d - 1
{txt}
{com}. 
. local controls DT_score_pre_std DT_time_pre rosen_pre_std cpcs_pre_std i.grade gend branch1 branch2 branch3 income_source1 income_source2 income_source3 income_source4 last_income_per_member hhmember hhheadage hhheadeduyear phone_survey age_tchr
{txt}
{com}. teffects psmatch (followup_cog_std) (treatment `controls') 
{res}
{txt}Treatment-effects estimation{col 48}Number of obs {col 67}= {res}       239
{txt:Estimator}{col 16}:{res: propensity-score matching}{col 48}{txt:Matches: requested }{col 67}{txt:=}          1
{txt:Outcome model}{col 16}:{res: matching}{txt}{col 63}min {col 67}= {res}         1
{txt:Treatment model}{col 16}:{res: logit}{col 63}{txt:max }{col 67}{txt:=}          1
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}   AI robust
{col 1}followup_c~d{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ATE          {txt}{c |}
{space 3}treatment {c |}
{space 3}(1 vs 0)  {c |}{col 14}{res}{space 2}-.3570091{col 26}{space 2} .1171502{col 37}{space 1}   -3.05{col 46}{space 3}0.002{col 54}{space 4}-.5866193{col 67}{space 3} -.127399
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. psmatch2 treatment `controls', outcome(followup_cog_std) noreplacement
{res}
{txt}{col 1}Probit regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:239}
{txt}{col 57}{lalign 13:LR chi2({res:19})}{col 70} = {res}{ralign 6:59.58}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-130.81459}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.1855}

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}             treatment{col 24}{c |} Coefficient{col 36}  Std. err.{col 48}      z{col 56}   P>|z|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}DT_score_pre_std {c |}{col 24}{res}{space 2}-.1665977{col 36}{space 2} .0966206{col 47}{space 1}   -1.72{col 56}{space 3}0.085{col 64}{space 4}-.3559705{col 77}{space 3} .0227752
{txt}{space 11}DT_time_pre {c |}{col 24}{res}{space 2}-.1793249{col 36}{space 2} .0624667{col 47}{space 1}   -2.87{col 56}{space 3}0.004{col 64}{space 4}-.3017573{col 77}{space 3}-.0568924
{txt}{space 9}rosen_pre_std {c |}{col 24}{res}{space 2}-.6571984{col 36}{space 2} .2259347{col 47}{space 1}   -2.91{col 56}{space 3}0.004{col 64}{space 4}-1.100022{col 77}{space 3}-.2143745
{txt}{space 10}cpcs_pre_std {c |}{col 24}{res}{space 2} .7994517{col 36}{space 2} .2264821{col 47}{space 1}    3.53{col 56}{space 3}0.000{col 64}{space 4} .3555549{col 77}{space 3} 1.243348
{txt}{space 15}4.grade {c |}{col 24}{res}{space 2} .2952241{col 36}{space 2} .2130287{col 47}{space 1}    1.39{col 56}{space 3}0.166{col 64}{space 4}-.1223044{col 77}{space 3} .7127526
{txt}{space 18}gend {c |}{col 24}{res}{space 2}-.0459502{col 36}{space 2} .1948532{col 47}{space 1}   -0.24{col 56}{space 3}0.814{col 64}{space 4}-.4278556{col 77}{space 3} .3359551
{txt}{space 15}branch1 {c |}{col 24}{res}{space 2}-.6016346{col 36}{space 2} .3093496{col 47}{space 1}   -1.94{col 56}{space 3}0.052{col 64}{space 4}-1.207949{col 77}{space 3} .0046794
{txt}{space 15}branch2 {c |}{col 24}{res}{space 2} .3124654{col 36}{space 2} .3595164{col 47}{space 1}    0.87{col 56}{space 3}0.385{col 64}{space 4}-.3921737{col 77}{space 3} 1.017105
{txt}{space 15}branch3 {c |}{col 24}{res}{space 2}-.6498064{col 36}{space 2} .2592846{col 47}{space 1}   -2.51{col 56}{space 3}0.012{col 64}{space 4}-1.157995{col 77}{space 3}-.1416179
{txt}{space 8}income_source1 {c |}{col 24}{res}{space 2} -.721011{col 36}{space 2} .7545196{col 47}{space 1}   -0.96{col 56}{space 3}0.339{col 64}{space 4}-2.199842{col 77}{space 3} .7578202
{txt}{space 8}income_source2 {c |}{col 24}{res}{space 2} .0092169{col 36}{space 2} .5904505{col 47}{space 1}    0.02{col 56}{space 3}0.988{col 64}{space 4}-1.148045{col 77}{space 3} 1.166479
{txt}{space 8}income_source3 {c |}{col 24}{res}{space 2}  -.23521{col 36}{space 2} .5681091{col 47}{space 1}   -0.41{col 56}{space 3}0.679{col 64}{space 4}-1.348683{col 77}{space 3} .8782634
{txt}{space 8}income_source4 {c |}{col 24}{res}{space 2} .9425426{col 36}{space 2} 1.746669{col 47}{space 1}    0.54{col 56}{space 3}0.589{col 64}{space 4}-2.480866{col 77}{space 3} 4.365951
{txt}last_income_per_member {c |}{col 24}{res}{space 2}-.0000735{col 36}{space 2} .0000887{col 47}{space 1}   -0.83{col 56}{space 3}0.407{col 64}{space 4}-.0002473{col 77}{space 3} .0001002
{txt}{space 14}hhmember {c |}{col 24}{res}{space 2} .1270295{col 36}{space 2} .0771431{col 47}{space 1}    1.65{col 56}{space 3}0.100{col 64}{space 4}-.0241683{col 77}{space 3} .2782273
{txt}{space 13}hhheadage {c |}{col 24}{res}{space 2}-.0044572{col 36}{space 2} .0102819{col 47}{space 1}   -0.43{col 56}{space 3}0.665{col 64}{space 4}-.0246093{col 77}{space 3}  .015695
{txt}{space 9}hhheadeduyear {c |}{col 24}{res}{space 2}-.0380874{col 36}{space 2} .0292059{col 47}{space 1}   -1.30{col 56}{space 3}0.192{col 64}{space 4}-.0953299{col 77}{space 3} .0191552
{txt}{space 10}phone_survey {c |}{col 24}{res}{space 2} .0133869{col 36}{space 2} .2189441{col 47}{space 1}    0.06{col 56}{space 3}0.951{col 64}{space 4}-.4157357{col 77}{space 3} .4425095
{txt}{space 14}age_tchr {c |}{col 24}{res}{space 2}-.0272352{col 36}{space 2} .0155042{col 47}{space 1}   -1.76{col 56}{space 3}0.079{col 64}{space 4} -.057623{col 77}{space 3} .0031525
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} 1.226203{col 36}{space 2} .8986663{col 47}{space 1}    1.36{col 56}{space 3}0.172{col 64}{space 4}-.5351508{col 77}{space 3} 2.987556
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}{hline 28}{c TT}{hline 59}
        Variable     Sample {c |}    Treated     Controls   Difference         S.E.   T-stat
{hline 28}{c +}{hline 59}
followup_cog_std  Unmatched {c |}{res} -.07508713   .131411283  -.206498412   .130743567    -1.58
{txt}{col 17}        ATT {c |}{res}-.210037912   .131411283  -.341449195   .140421087    -2.43
{txt}{hline 28}{c +}{hline 59}
Note: S.E. does not take into account that the propensity score is estimated.

 psmatch2: {c |}   psmatch2: Common
 Treatment {c |}        support
assignment {c |} Off suppo  On suppor {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
 Untreated {c |}{res}         0         95 {txt}{c |}{res}        95 
{txt}   Treated {c |}{res}        49         95 {txt}{c |}{res}       144 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}        49        190 {txt}{c |}{res}       239 
{txt}
{com}. gen psmattrition = 1 if _support!=1
{txt}(190 missing values generated)

{com}. recode psmattrition (.=0)
{txt}(190 changes made to {bf:psmattrition})

{com}. keep if psmattrition == 0
{txt}(53 observations deleted)

{com}. 
. egen DT_score_pre_std_med = median(DT_score_pre_std)
{txt}
{com}. gen DT_score_pre_std_upper50 = 1 if DT_score_pre_std>DT_score_pre_std_med
{txt}(95 missing values generated)

{com}. recode DT_score_pre_std_upper50 (.=0)
{txt}(95 changes made to {bf:DT_score_pre_std_upper50})

{com}. 
. egen rosen_pre_std_med = median(rosen_pre_std)
{txt}
{com}. gen rosen_pre_std_upper50 = 1 if rosen_pre_std>rosen_pre_std_med
{txt}(106 missing values generated)

{com}. recode rosen_pre_std_upper50 (.=0)
{txt}(106 changes made to {bf:rosen_pre_std_upper50})

{com}. rename rosen_pre_std_upper50 RSES_std_upper50
{res}{txt}
{com}. 
. egen cpcs_pre_std_med = median(cpcs_pre_std)
{txt}
{com}. gen cpcs_pre_std_upper50 = 1 if cpcs_pre_std>cpcs_pre_std_med
{txt}(117 missing values generated)

{com}. recode cpcs_pre_std_upper50 (.=0)
{txt}(117 changes made to {bf:cpcs_pre_std_upper50})

{com}. rename cpcs_pre_std_upper50 CPCS_std_upper50
{res}{txt}
{com}. 
. 
. /// Cognitive
> wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 95
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 27
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}3.5
{col 69}{txt}max{col 72} = {res} 10
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.4757676{col 38}{space 1}  -2.07{col 46}{space 3}0.036{col 54}{space 3}-1.029574{col 66}{space 3}-.0221115
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}.         
. scalar n_cog_u = e(N)
{txt}
{com}. scalar n_clust_cog_u = e(N_clust)
{txt}
{com}. matrix r2_followup_cog_std_temp = r(table)
{txt}
{com}. 
. 
. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix r2_followup_cog_std_upper_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix r2_followup_cog_std_upper_mean[1,`j'] = r2_followup_cog_std_temp[1,`j']
{txt}  3{com}. * standard error
. matrix r2_followup_cog_std_upper_se[1,`j'] = r2_followup_cog_std_temp[2,`j']
{txt}  4{com}. * p value
. matrix r2_followup_cog_std_upper_pv[1,`j'] = r2_followup_cog_std_temp[3,`j']
{txt}  5{com}. {c )-}
{txt}
{com}. 
. 
. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 95
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 27
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}3.5
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.1998601{col 38}{space 1}  -0.76{col 46}{space 3}0.490{col 54}{space 3}-.7683655{col 66}{space 3} .3268983
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. scalar n_cog_l = e(N)
{txt}
{com}. scalar n_clust_cog_l = e(N_clust)
{txt}
{com}. matrix r2_followup_cog_std_temp = r(table)
{txt}
{com}. 
. 
. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix r2_followup_cog_std_lower_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix r2_followup_cog_std_lower_mean[1,`j'] = r2_followup_cog_std_temp[1,`j']
{txt}  3{com}. * standard error
. matrix r2_followup_cog_std_lower_se[1,`j'] = r2_followup_cog_std_temp[2,`j']
{txt}  4{com}. * p value
. matrix r2_followup_cog_std_lower_pv[1,`j'] = r2_followup_cog_std_temp[3,`j']
{txt}  5{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std i.treatment##i.DT_score_pre_std_upper50, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:1.treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:1.treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:1.DT_score_pre_std_upper50 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:1.DT_score_pre_std_upper50}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:1.treatment#1.DT_score_pre_std_upper50 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:1.treatment#1.DT_score_pre_std_upper50}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}190
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 31
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}6.1
{col 69}{txt}max{col 72} = {res} 12
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraints             {col 26}{c |}
{res}{col 1}{text}         1.treatment = 0{col 26}{c |}{result}{space 2}-.1998601{col 38}{space 1}  -0.76{col 46}{space 3}0.536{col 54}{space 3}-.7760648{col 66}{space 3}  .368268
{col 1}{text}1.DT_score_pre_std_upper{col 26}{c |}
{res}{col 1}{text}                  50 = 0{col 26}{c |}{result}{space 2} .1872903{col 38}{space 1}   0.90{col 46}{space 3}0.350{col 54}{space 3}-.3218947{col 66}{space 3} .7712105
{col 26}{text}{c |}
{res}{col 1}{text}            1.treatment#{col 26}{c |}
{res}{col 1}{text}1.DT_score_pre_std_upper{col 26}{c |}
{res}{col 1}{text}                  50 = 0{col 26}{c |}{result}{space 2}-.2759075{col 38}{space 1}  -0.84{col 46}{space 3}0.382{col 54}{space 3}-.9832124{col 66}{space 3} .4321568
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix cog_difference = r(table)
{txt}
{com}.     
. /// Non cognitive
> 
. 
. foreach dep in RSES_std CPCS_std{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment if `dep'_upper50 == 1, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         scalar n_`dep'_u = e(N)
{txt}  4{com}.         scalar n_clust_`dep'_u = e(N_clust)
{txt}  5{com}.         matrix r2_`dep'_temp = r(table)
{txt}  6{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  7{com}.                 matrix r2_`dep'_upper_`s' = J(1,2,.)
{txt}  8{com}.         {c )-}
{txt}  9{com}. 
.         foreach j in 1 2 {c -(}
{txt} 10{com}.         * beta
.         matrix r2_`dep'_upper_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt} 11{com}.         * standard error
.         matrix r2_`dep'_upper_se[1,`j'] = r2_`dep'_temp[2,`j']
{txt} 12{com}.         * p value
.         matrix r2_`dep'_upper_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 13{com}.         {c )-}
{txt} 14{com}. 
. 
.         wildbootstrap reg `dep' treatment if `dep'_upper50 == 0, cluster(school_no) reps(1000)
{txt} 15{com}.         
.         scalar n_`dep'_l = e(N)
{txt} 16{com}.         scalar n_clust_`dep'_l = e(N_clust)
{txt} 17{com}.         matrix r2_`dep'_temp = r(table)
{txt} 18{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt} 19{com}.                 matrix r2_`dep'_lower_`s' = J(1,2,.)
{txt} 20{com}.         {c )-}
{txt} 21{com}. 
.         foreach j in 1 2 {c -(}
{txt} 22{com}.         * beta
.         matrix r2_`dep'_lower_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt} 23{com}.         * standard error
.         matrix r2_`dep'_lower_se[1,`j'] = r2_`dep'_temp[2,`j']
{txt} 24{com}.         * p value
.         matrix r2_`dep'_lower_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 25{com}.         {c )-}
{txt} 26{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 82
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 25
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}3.3
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} 1.115972{col 38}{space 1}   4.54{col 46}{space 3}0.000{col 54}{space 3} .5582586{col 66}{space 3} 1.641307
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}102
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 27
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}3.8
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2047219{col 38}{space 1}   0.72{col 46}{space 3}0.494{col 54}{space 3}-.4089326{col 66}{space 3} .8153609
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 71
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 26
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .8628595{col 38}{space 1}   3.88{col 46}{space 3}0.006{col 54}{space 3} .3927783{col 66}{space 3} 1.340037
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}113
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 26
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}4.3
{col 69}{txt}max{col 72} = {res} 10
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .4763509{col 38}{space 1}   1.48{col 46}{space 3}0.144{col 54}{space 3}-.2730726{col 66}{space 3}  1.21944
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. wildbootstrap reg RSES_std i.treatment##i.RSES_std_upper50, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:1.treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:1.treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:1.RSES_std_upper50 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:1.RSES_std_upper50}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:1.treatment#1.RSES_std_upper50 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:1.treatment#1.RSES_std_upper50}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}184
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 31
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}5.9
{col 69}{txt}max{col 72} = {res} 12
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraints             {col 26}{c |}
{res}{col 1}{text}         1.treatment = 0{col 26}{c |}{result}{space 2} .2047219{col 38}{space 1}   0.72{col 46}{space 3}0.504{col 54}{space 3}-.4292858{col 66}{space 3} .8015068
{col 1}{text}  1.RSES_std_upper50 = 0{col 26}{c |}{result}{space 2}-.5098745{col 38}{space 1}  -3.60{col 46}{space 3}0.006{col 54}{space 3}-.7878528{col 66}{space 3}-.2180876
{col 1}{text}            1.treatment#{col 26}{c |}
{res}{col 1}{text}  1.RSES_std_upper50 = 0{col 26}{c |}{result}{space 2} .9112506{col 38}{space 1}   2.91{col 46}{space 3}0.006{col 54}{space 3} .2966825{col 66}{space 3} 1.660529
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix RSES_difference = r(table)
{txt}
{com}. 
. wildbootstrap reg CPCS_std i.treatment##i.CPCS_std_upper50, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:1.treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:1.treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:19})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:1.CPCS_std_upper50 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:1.CPCS_std_upper50}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:1.treatment#1.CPCS_std_upper50 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:1.treatment#1.CPCS_std_upper50}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}184
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 31
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}5.9
{col 69}{txt}max{col 72} = {res} 12
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraints             {col 26}{c |}
{res}{col 1}{text}         1.treatment = 0{col 26}{c |}{result}{space 2} .4763509{col 38}{space 1}   1.48{col 46}{space 3}0.198{col 54}{space 3}-.2668307{col 66}{space 3} 1.171093
{col 1}{text}  1.CPCS_std_upper50 = 0{col 26}{c |}{result}{space 2}-.2711558{col 38}{space 1}  -1.22{col 46}{space 3}0.246{col 54}{space 3}-.7153298{col 66}{space 3} .2520124
{col 1}{text}            1.treatment#{col 26}{c |}
{res}{col 1}{text}  1.CPCS_std_upper50 = 0{col 26}{c |}{result}{space 2} .3865087{col 38}{space 1}   1.15{col 46}{space 3}0.284{col 54}{space 3} -.364493{col 66}{space 3} 1.102566
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix CPCS_difference = r(table)
{txt}
{com}. 
. // significant level
. 
. local outcome followup_cog_std RSES_std CPCS_std
{txt}
{com}. local hetero upper lower
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. foreach h in `hetero'{c -(}
{txt}  3{com}.                 if r2_`dep'_`h'_pv[1,1]<=0.01 {c -(}
{txt}  4{com}.                         local star_`dep'_`h' %3s "***"
{txt}  5{com}.                 {c )-}
{txt}  6{com}.                 else if (r2_`dep'_`h'_pv[1,1]>0.01) & (r2_`dep'_`h'_pv[1,1]<=0.05) {c -(}
{txt}  7{com}.                         local star_`dep'_`h' %2s "**"
{txt}  8{com}.                 {c )-}
{txt}  9{com}.                 else if (r2_`dep'_`h'_pv[1,1]>0.05) & (r2_`dep'_`h'_pv[1,1]<=0.10) {c -(}
{txt} 10{com}.                         local star_`dep'_`h' %1s "*"
{txt} 11{com}.                 {c )-}
{txt} 12{com}.                 else {c -(}
{txt} 13{com}.                         local star_`dep'_`h'  ""
{txt} 14{com}.                 {c )-}
{txt} 15{com}. {c )-} 
{txt} 16{com}. {c )-}
{txt}
{com}. 
. 
. 
. /// Table
> tempname hh2
{txt}
{com}. file open `hh2' using "$path_output/hetero_matchsample.tex", write replace
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "% Author: Kazuma Takakura" _newline
{txt}
{com}. file write `hh2' "% Date: `c(current_date)'" _newline
{txt}
{com}. file write `hh2' "% Time: `c(current_time)'" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. 
. file write `hh2' "\begin{c -(}table{c )-}[h!]\footnotesize" _newline
{txt}
{com}. file write `hh2' "  \centering" _newline
{txt}
{com}. file write `hh2' "  \caption{c -(}Heterogeneity among Baseline Abilites (Matched Sample){c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:hetero_match{c )-}" _newline
{txt}
{com}. file write `hh2' "\scalebox{c -(}1{c )-}{c -(}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}threeparttable{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "\begin{c -(}tabular{c )-}{c -(}lccc{c )-}\toprule" _newline
{txt}
{com}. 
. file write `hh2' "  & Top 50\%^{c -(}b{c )-}  & Bottom 50\%^{c -(}b{c )-} & Differences  \\\midrule" _newline
{txt}
{com}. file write `hh2' "\multicolumn{c -(}4{c )-}{c -(}c{c )-}{c -(}Panel A: Rapid math test score^{c -(}a{c )-}{c )-}\\\midrule" _newline
{txt}
{com}. file write `hh2' " Treatment & " %04.3f (r2_followup_cog_std_upper_mean[1,1]) `star_followup_cog_std_upper' "  & " %04.3f (r2_followup_cog_std_lower_mean[1,1]) `star_followup_cog_std_lower' " &  " %04.3f (cog_difference[1,3]) " \\" _newline
{txt}
{com}. file write `hh2' " & ( " %04.3f (r2_followup_cog_std_upper_pv[1,1]) " ) & ( " %04.3f (r2_followup_cog_std_lower_pv[1,1]) " ) & ( " %04.3f (cog_difference[3,3]) " ) \\ " _newline
{txt}
{com}. file write `hh2' " Observation &  " %02.0f ( n_cog_u ) " & " %02.0f ( n_cog_l ) " &  \\ " _newline
{txt}
{com}. file write `hh2' " N of clusters &  " %02.0f ( n_clust_cog_u ) " & " %02.0f ( n_clust_cog_l ) " &  \\\midrule " _newline
{txt}
{com}. 
. file write `hh2' "\multicolumn{c -(}4{c )-}{c -(}c{c )-}{c -(}Panel B: RSES score^{c -(}a{c )-}{c )-}\\\midrule" _newline
{txt}
{com}. file write `hh2' " Treatment & " %04.3f (r2_RSES_std_upper_mean[1,1]) `star_RSES_std_upper' "  & " %04.3f (r2_RSES_std_lower_mean[1,1]) `star_RSES_std_lower' " &  " %04.3f (RSES_difference[1,3]) " *** \\" _newline
{txt}
{com}. file write `hh2' " & ( " %04.3f (r2_RSES_std_upper_pv[1,1]) " ) & ( " %04.3f (r2_RSES_std_lower_pv[1,1]) " ) & ( " %04.3f (RSES_difference[3,3]) " ) \\ " _newline
{txt}
{com}. file write `hh2' " Observation &  " %02.0f ( n_RSES_std_u ) " & " %02.0f ( n_RSES_std_l ) " &  \\ " _newline
{txt}
{com}. file write `hh2' " N of clusters &  " %02.0f ( n_clust_RSES_std_u ) " & " %02.0f ( n_clust_RSES_std_l ) " &  \\\midrule " _newline
{txt}
{com}. 
. file write `hh2' "\multicolumn{c -(}4{c )-}{c -(}c{c )-}{c -(}Panel C: CPCS score^{c -(}a{c )-}{c )-}\\\midrule" _newline
{txt}
{com}. file write `hh2' " Treatment & " %04.3f (r2_CPCS_std_upper_mean[1,1]) `star_CPCS_std_upper' "  & " %04.3f (r2_CPCS_std_lower_mean[1,1]) `star_CPCS_std_lower' " &  " %04.3f (CPCS_difference[1,3]) " \\" _newline
{txt}
{com}. file write `hh2' " & ( " %04.3f (r2_CPCS_std_upper_pv[1,1]) " ) & ( " %04.3f (r2_CPCS_std_lower_pv[1,1]) " ) & ( " %04.3f (CPCS_difference[3,3]) " ) \\ " _newline
{txt}
{com}. file write `hh2' " Observation &  " %02.0f ( n_CPCS_std_u ) " & " %02.0f ( n_CPCS_std_l ) " &  \\ " _newline
{txt}
{com}. file write `hh2' " N of clusters &  " %02.0f ( n_clust_CPCS_std_u ) " & " %02.0f ( n_clust_CPCS_std_l ) " &  \\\midrule " _newline
{txt}
{com}. 
. file write `hh2' "\end{c -(}tabular{c )-}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\item (a) Dependent variables are standardized using the average and variance of the whole follow-up sample in the February 2022 survey." _newline
{txt}
{com}. file write `hh2' "\item (b) Cutoffs are created based on whether their ability to perform each item at the baseline was higher or lower than the median. " _newline
{txt}
{com}. file write `hh2' "\item (c) Wild cluster bootstrap p-values are reported within parentheses. Clusters are schools at the baseline. " _newline
{txt}
{com}. file write `hh2' "\item (d) $^*$ Significant at 10\% level; $^{c -(}**{c )-}$ significant at 5\% level; $^{c -(}***{c )-}$ significant at 1\% level. " _newline
{txt}
{com}. file write `hh2' "\end{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}threeparttable{c )-}" _newline
{txt}
{com}. file write `hh2' "{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:addlabel{c )-}%" _newline
{txt}
{com}. file write `hh2' "\end{c -(}table{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. file close `hh2'
{txt}
{com}. 
{txt}end of do-file

{com}. 
. do "$path_do/3_table_C4_C5.do"
{txt}
{com}. * This is the do file to create "Table C4. Heterogeneity across Baseline Abilites (Matched Sample, Math and CPCS)" and "Table C5. Heterogeneity across Baseline Abilites (Matched Sample, Math and RSES)"
. set seed 123
{txt}
{com}. 
. use "$path_data/temp/followup_student_parents_matched", clear
{txt}
{com}. 
. gen gend = q1d - 1
{txt}
{com}. 
. local controls DT_score_pre_std DT_time_pre rosen_pre_std cpcs_pre_std i.grade gend branch1 branch2 branch3 income_source1 income_source2 income_source3 income_source4 last_income_per_member hhmember hhheadage hhheadeduyear phone_survey age_tchr
{txt}
{com}. teffects psmatch (followup_cog_std) (treatment `controls') 
{res}
{txt}Treatment-effects estimation{col 48}Number of obs {col 67}= {res}       239
{txt:Estimator}{col 16}:{res: propensity-score matching}{col 48}{txt:Matches: requested }{col 67}{txt:=}          1
{txt:Outcome model}{col 16}:{res: matching}{txt}{col 63}min {col 67}= {res}         1
{txt:Treatment model}{col 16}:{res: logit}{col 63}{txt:max }{col 67}{txt:=}          1
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}   AI robust
{col 1}followup_c~d{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ATE          {txt}{c |}
{space 3}treatment {c |}
{space 3}(1 vs 0)  {c |}{col 14}{res}{space 2}-.3570091{col 26}{space 2} .1171502{col 37}{space 1}   -3.05{col 46}{space 3}0.002{col 54}{space 4}-.5866193{col 67}{space 3} -.127399
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. psmatch2 treatment `controls', outcome(followup_cog_std) noreplacement
{res}
{txt}{col 1}Probit regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:239}
{txt}{col 57}{lalign 13:LR chi2({res:19})}{col 70} = {res}{ralign 6:59.58}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-130.81459}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.1855}

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}             treatment{col 24}{c |} Coefficient{col 36}  Std. err.{col 48}      z{col 56}   P>|z|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}DT_score_pre_std {c |}{col 24}{res}{space 2}-.1665977{col 36}{space 2} .0966206{col 47}{space 1}   -1.72{col 56}{space 3}0.085{col 64}{space 4}-.3559705{col 77}{space 3} .0227752
{txt}{space 11}DT_time_pre {c |}{col 24}{res}{space 2}-.1793249{col 36}{space 2} .0624667{col 47}{space 1}   -2.87{col 56}{space 3}0.004{col 64}{space 4}-.3017573{col 77}{space 3}-.0568924
{txt}{space 9}rosen_pre_std {c |}{col 24}{res}{space 2}-.6571984{col 36}{space 2} .2259347{col 47}{space 1}   -2.91{col 56}{space 3}0.004{col 64}{space 4}-1.100022{col 77}{space 3}-.2143745
{txt}{space 10}cpcs_pre_std {c |}{col 24}{res}{space 2} .7994517{col 36}{space 2} .2264821{col 47}{space 1}    3.53{col 56}{space 3}0.000{col 64}{space 4} .3555549{col 77}{space 3} 1.243348
{txt}{space 15}4.grade {c |}{col 24}{res}{space 2} .2952241{col 36}{space 2} .2130287{col 47}{space 1}    1.39{col 56}{space 3}0.166{col 64}{space 4}-.1223044{col 77}{space 3} .7127526
{txt}{space 18}gend {c |}{col 24}{res}{space 2}-.0459502{col 36}{space 2} .1948532{col 47}{space 1}   -0.24{col 56}{space 3}0.814{col 64}{space 4}-.4278556{col 77}{space 3} .3359551
{txt}{space 15}branch1 {c |}{col 24}{res}{space 2}-.6016346{col 36}{space 2} .3093496{col 47}{space 1}   -1.94{col 56}{space 3}0.052{col 64}{space 4}-1.207949{col 77}{space 3} .0046794
{txt}{space 15}branch2 {c |}{col 24}{res}{space 2} .3124654{col 36}{space 2} .3595164{col 47}{space 1}    0.87{col 56}{space 3}0.385{col 64}{space 4}-.3921737{col 77}{space 3} 1.017105
{txt}{space 15}branch3 {c |}{col 24}{res}{space 2}-.6498064{col 36}{space 2} .2592846{col 47}{space 1}   -2.51{col 56}{space 3}0.012{col 64}{space 4}-1.157995{col 77}{space 3}-.1416179
{txt}{space 8}income_source1 {c |}{col 24}{res}{space 2} -.721011{col 36}{space 2} .7545196{col 47}{space 1}   -0.96{col 56}{space 3}0.339{col 64}{space 4}-2.199842{col 77}{space 3} .7578202
{txt}{space 8}income_source2 {c |}{col 24}{res}{space 2} .0092169{col 36}{space 2} .5904505{col 47}{space 1}    0.02{col 56}{space 3}0.988{col 64}{space 4}-1.148045{col 77}{space 3} 1.166479
{txt}{space 8}income_source3 {c |}{col 24}{res}{space 2}  -.23521{col 36}{space 2} .5681091{col 47}{space 1}   -0.41{col 56}{space 3}0.679{col 64}{space 4}-1.348683{col 77}{space 3} .8782634
{txt}{space 8}income_source4 {c |}{col 24}{res}{space 2} .9425426{col 36}{space 2} 1.746669{col 47}{space 1}    0.54{col 56}{space 3}0.589{col 64}{space 4}-2.480866{col 77}{space 3} 4.365951
{txt}last_income_per_member {c |}{col 24}{res}{space 2}-.0000735{col 36}{space 2} .0000887{col 47}{space 1}   -0.83{col 56}{space 3}0.407{col 64}{space 4}-.0002473{col 77}{space 3} .0001002
{txt}{space 14}hhmember {c |}{col 24}{res}{space 2} .1270295{col 36}{space 2} .0771431{col 47}{space 1}    1.65{col 56}{space 3}0.100{col 64}{space 4}-.0241683{col 77}{space 3} .2782273
{txt}{space 13}hhheadage {c |}{col 24}{res}{space 2}-.0044572{col 36}{space 2} .0102819{col 47}{space 1}   -0.43{col 56}{space 3}0.665{col 64}{space 4}-.0246093{col 77}{space 3}  .015695
{txt}{space 9}hhheadeduyear {c |}{col 24}{res}{space 2}-.0380874{col 36}{space 2} .0292059{col 47}{space 1}   -1.30{col 56}{space 3}0.192{col 64}{space 4}-.0953299{col 77}{space 3} .0191552
{txt}{space 10}phone_survey {c |}{col 24}{res}{space 2} .0133869{col 36}{space 2} .2189441{col 47}{space 1}    0.06{col 56}{space 3}0.951{col 64}{space 4}-.4157357{col 77}{space 3} .4425095
{txt}{space 14}age_tchr {c |}{col 24}{res}{space 2}-.0272352{col 36}{space 2} .0155042{col 47}{space 1}   -1.76{col 56}{space 3}0.079{col 64}{space 4} -.057623{col 77}{space 3} .0031525
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} 1.226203{col 36}{space 2} .8986663{col 47}{space 1}    1.36{col 56}{space 3}0.172{col 64}{space 4}-.5351508{col 77}{space 3} 2.987556
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}{hline 28}{c TT}{hline 59}
        Variable     Sample {c |}    Treated     Controls   Difference         S.E.   T-stat
{hline 28}{c +}{hline 59}
followup_cog_std  Unmatched {c |}{res} -.07508713   .131411283  -.206498412   .130743567    -1.58
{txt}{col 17}        ATT {c |}{res}-.210037912   .131411283  -.341449195   .140421087    -2.43
{txt}{hline 28}{c +}{hline 59}
Note: S.E. does not take into account that the propensity score is estimated.

 psmatch2: {c |}   psmatch2: Common
 Treatment {c |}        support
assignment {c |} Off suppo  On suppor {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
 Untreated {c |}{res}         0         95 {txt}{c |}{res}        95 
{txt}   Treated {c |}{res}        49         95 {txt}{c |}{res}       144 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}        49        190 {txt}{c |}{res}       239 
{txt}
{com}. gen psmattrition = 1 if _support!=1
{txt}(190 missing values generated)

{com}. recode psmattrition (.=0)
{txt}(190 changes made to {bf:psmattrition})

{com}. keep if psmattrition == 0
{txt}(53 observations deleted)

{com}. 
. egen DT_score_pre_std_med = median(DT_score_pre_std)
{txt}
{com}. gen DT_score_pre_std_upper50 = 1 if DT_score_pre_std>DT_score_pre_std_med
{txt}(95 missing values generated)

{com}. recode DT_score_pre_std_upper50 (.=0)
{txt}(95 changes made to {bf:DT_score_pre_std_upper50})

{com}. 
. egen rosen_pre_std_med = median(rosen_pre_std)
{txt}
{com}. gen rosen_pre_std_upper50 = 1 if rosen_pre_std>rosen_pre_std_med
{txt}(106 missing values generated)

{com}. recode rosen_pre_std_upper50 (.=0)
{txt}(106 changes made to {bf:rosen_pre_std_upper50})

{com}. rename rosen_pre_std_upper50 RSES_std_upper50
{res}{txt}
{com}. 
. egen cpcs_pre_std_med = median(cpcs_pre_std)
{txt}
{com}. gen cpcs_pre_std_upper50 = 1 if cpcs_pre_std>cpcs_pre_std_med
{txt}(117 missing values generated)

{com}. recode cpcs_pre_std_upper50 (.=0)
{txt}(117 changes made to {bf:cpcs_pre_std_upper50})

{com}. rename cpcs_pre_std_upper50 CPCS_std_upper50
{res}{txt}
{com}. 
. 
. /// Cognitive
> wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 42
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 20
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.1
{col 69}{txt}max{col 72} = {res}  6
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.4211413{col 38}{space 1}  -1.36{col 46}{space 3}0.194{col 54}{space 3}-1.069149{col 66}{space 3} .2188651
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_rses_u = e(N)
{txt}
{com}. scalar n_clust_cog_u_rses_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  6
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.5173232{col 38}{space 1}  -1.81{col 46}{space 3}0.100{col 54}{space 3}-1.152199{col 66}{space 3} .1505739
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_rses_l = e(N)
{txt}
{com}. scalar n_clust_cog_u_rses_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 42
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 19
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.2
{col 69}{txt}max{col 72} = {res}  6
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.3343013{col 38}{space 1}  -1.19{col 46}{space 3}0.280{col 54}{space 3}-.9628536{col 66}{space 3} .3172264
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_rses_u = e(N)
{txt}
{com}. scalar n_clust_cog_l_rses_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 20
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  6
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0901722{col 38}{space 1}  -0.28{col 46}{space 3}0.792{col 54}{space 3}-.8140386{col 66}{space 3} .6310287
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_rses_l = e(N)
{txt}
{com}. scalar n_clust_cog_l_rses_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 39
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 19
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.1
{col 69}{txt}max{col 72} = {res}  4
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0839484{col 38}{space 1}  -0.25{col 46}{space 3}0.752{col 54}{space 3}-.8048836{col 66}{space 3}  .675766
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_cpcs_u = e(N)
{txt}
{com}. scalar n_clust_cog_u_cpcs_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:19})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 22
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.5
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.7485617{col 38}{space 1}  -2.52{col 46}{space 3}0.030{col 54}{space 3}-1.458907{col 66}{space 3}-.0907209
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_cpcs_l = e(N)
{txt}
{com}. scalar n_clust_cog_u_cpcs_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 34
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 19
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}1.8
{col 69}{txt}max{col 72} = {res}  6
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0940222{col 38}{space 1}  -0.29{col 46}{space 3}0.762{col 54}{space 3}-.7690856{col 66}{space 3} .7448919
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_cpcs_u = e(N)
{txt}
{com}. scalar n_clust_cog_l_cpcs_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 61
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.9
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.1511072{col 38}{space 1}  -0.51{col 46}{space 3}0.634{col 54}{space 3}-.8519789{col 66}{space 3} .4309905
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_cpcs_l = e(N)
{txt}
{com}. scalar n_clust_cog_l_cpcs_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. 
. 
. /// RSES
> wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:19})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 40
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 19
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.1
{col 69}{txt}max{col 72} = {res}  6
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  1.56962{col 38}{space 1}   4.49{col 46}{space 3}0.000{col 54}{space 3} .8623161{col 66}{space 3} 2.349416
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_u_cog_u = e(N)
{txt}
{com}. scalar n_clust_rses_u_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 51
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.2
{col 69}{txt}max{col 72} = {res}  6
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  .003041{col 38}{space 1}   0.01{col 46}{space 3}0.972{col 54}{space 3}-.6302692{col 66}{space 3} .5607139
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_l_cog_u = e(N)
{txt}
{com}. scalar n_clust_rses_l_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 42
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 19
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.2
{col 69}{txt}max{col 72} = {res}  6
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .6698299{col 38}{space 1}   2.01{col 46}{space 3}0.080{col 54}{space 3}-.1688323{col 66}{space 3} 1.379072
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_u_cog_l = e(N)
{txt}
{com}. scalar n_clust_rses_u_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 51
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 20
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.5
{col 69}{txt}max{col 72} = {res}  6
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .4066245{col 38}{space 1}   1.06{col 46}{space 3}0.332{col 54}{space 3}-.5736529{col 66}{space 3} 1.285733
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_l_cog_l = e(N)
{txt}
{com}. scalar n_clust_rses_l_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 37
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 18
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.1
{col 69}{txt}max{col 72} = {res}  4
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} 1.023921{col 38}{space 1}   3.52{col 46}{space 3}0.004{col 54}{space 3} .3735666{col 66}{space 3} 1.656095
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 54
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 22
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.5
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .4597985{col 38}{space 1}   1.35{col 46}{space 3}0.214{col 54}{space 3}-.3266624{col 66}{space 3} 1.180652
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 34
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 19
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}1.8
{col 69}{txt}max{col 72} = {res}  6
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .5040754{col 38}{space 1}   1.35{col 46}{space 3}0.222{col 54}{space 3}-.5212223{col 66}{space 3}  1.27578
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 59
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  .566956{col 38}{space 1}   1.55{col 46}{space 3}0.204{col 54}{space 3}-.3061569{col 66}{space 3}  1.32137
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. 
. 
. /// CPCS
> 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 40
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 19
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.1
{col 69}{txt}max{col 72} = {res}  6
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} 1.608556{col 38}{space 1}   5.01{col 46}{space 3}0.002{col 54}{space 3} .8944085{col 66}{space 3} 2.280671
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 51
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.2
{col 69}{txt}max{col 72} = {res}  6
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0201508{col 38}{space 1}  -0.08{col 46}{space 3}0.998{col 54}{space 3}-.5845091{col 66}{space 3} .6022662
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 42
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 19
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.2
{col 69}{txt}max{col 72} = {res}  6
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .6837825{col 38}{space 1}   2.09{col 46}{space 3}0.102{col 54}{space 3} -.156381{col 66}{space 3} 1.396096
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 51
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 20
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.5
{col 69}{txt}max{col 72} = {res}  6
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3984782{col 38}{space 1}   0.97{col 46}{space 3}0.384{col 54}{space 3}-.6045223{col 66}{space 3} 1.309879
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 37
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 18
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.1
{col 69}{txt}max{col 72} = {res}  4
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} 1.130813{col 38}{space 1}   4.12{col 46}{space 3}0.000{col 54}{space 3}  .568248{col 66}{space 3} 1.739764
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_u_cog_u = e(N)
{txt}
{com}. scalar n_clust_cpcs_u_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 54
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 22
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.5
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  .383872{col 38}{space 1}   1.18{col 46}{space 3}0.250{col 54}{space 3}-.3338735{col 66}{space 3}  1.10889
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_l_cog_u = e(N)
{txt}
{com}. scalar n_clust_cpcs_l_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 34
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 19
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}1.8
{col 69}{txt}max{col 72} = {res}  6
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .5263379{col 38}{space 1}   1.36{col 46}{space 3}0.230{col 54}{space 3}-.3838779{col 66}{space 3} 1.403849
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_u_cog_l = e(N)
{txt}
{com}. scalar n_clust_cpcs_u_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 59
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .5639131{col 38}{space 1}   1.51{col 46}{space 3}0.238{col 54}{space 3}-.3211892{col 66}{space 3} 1.318318
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_l_cog_l = e(N)
{txt}
{com}. scalar n_clust_cpcs_l_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. // significant level
. 
. 
. local outcome cog rses cpcs
{txt}
{com}. local hetero1 cogU cogL
{txt}
{com}. local hetero2 rsesU rsesL
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. foreach h1 in `hetero1'{c -(}
{txt}  3{com}. foreach h2 in `hetero2'{c -(}
{txt}  4{com}.                 if `dep'_`h1'_`h2'_pv[1,1]<=0.01 {c -(}
{txt}  5{com}.                         local star_`dep'_`h1'_`h2' %3s "***"
{txt}  6{com}.                 {c )-}
{txt}  7{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.01) & (`dep'_`h1'_`h2'_pv[1,1]<=0.05) {c -(}
{txt}  8{com}.                         local star_`dep'_`h1'_`h2' %2s "**"
{txt}  9{com}.                 {c )-}
{txt} 10{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.05) & (`dep'_`h1'_`h2'_pv[1,1]<=0.10) {c -(}
{txt} 11{com}.                         local star_`dep'_`h1'_`h2' %1s "*"
{txt} 12{com}.                 {c )-}
{txt} 13{com}.                 else {c -(}
{txt} 14{com}.                         local star_`dep'_`h1'_`h2'  ""
{txt} 15{com}.                 {c )-}
{txt} 16{com}. {c )-} 
{txt} 17{com}. {c )-}
{txt} 18{com}. {c )-}
{txt}
{com}. 
. local outcome cog rses cpcs
{txt}
{com}. local hetero1 cogU cogL
{txt}
{com}. local hetero2 cpcsU cpcsL
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. foreach h1 in `hetero1'{c -(}
{txt}  3{com}. foreach h2 in `hetero2'{c -(}
{txt}  4{com}.                 if `dep'_`h1'_`h2'_pv[1,1]<=0.01 {c -(}
{txt}  5{com}.                         local star_`dep'_`h1'_`h2' %3s "***"
{txt}  6{com}.                 {c )-}
{txt}  7{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.01) & (`dep'_`h1'_`h2'_pv[1,1]<=0.05) {c -(}
{txt}  8{com}.                         local star_`dep'_`h1'_`h2' %2s "**"
{txt}  9{com}.                 {c )-}
{txt} 10{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.05) & (`dep'_`h1'_`h2'_pv[1,1]<=0.10) {c -(}
{txt} 11{com}.                         local star_`dep'_`h1'_`h2' %1s "*"
{txt} 12{com}.                 {c )-}
{txt} 13{com}.                 else {c -(}
{txt} 14{com}.                         local star_`dep'_`h1'_`h2'  ""
{txt} 15{com}.                 {c )-}
{txt} 16{com}. {c )-} 
{txt} 17{com}. {c )-}
{txt} 18{com}. {c )-}
{txt}
{com}. 
. /// Table
> 
. 
. 
. tempname hh2
{txt}
{com}. file open `hh2' using "$path_output/hetero_2by2_RSES_matchsample.tex", write replace
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "% Author: Kazuma Takakura" _newline
{txt}
{com}. file write `hh2' "% Date: `c(current_date)'" _newline
{txt}
{com}. file write `hh2' "% Time: `c(current_time)'" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. 
. file write `hh2' "\begin{c -(}table{c )-}[h!]\footnotesize" _newline
{txt}
{com}. file write `hh2' "  \centering" _newline
{txt}
{com}. file write `hh2' "  \caption{c -(}Heterogeneity among Baseline Abilites (Matched Sample, Math and RSES){c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:hetero_rses_match{c )-}" _newline
{txt}
{com}. file write `hh2' "\scalebox{c -(}1{c )-}{c -(}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}threeparttable{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "\begin{c -(}tabular{c )-}{c -(}lccccccc{c )-}\toprule" _newline
{txt}
{com}. 
.  
. 
.   
. file write `hh2' " Dependent Variable & \multicolumn{c -(}2{c )-}{c -(}c{c )-}{c -(}Baseline^{c -(}b{c )-}{c )-} & Difference & Obs & N of clusters \\\midrule\midrule" _newline
{txt}
{com}. file write `hh2' " Rapid math test score^{c -(}a{c )-} & Math Top 50\%  & RSES Top 50\% & " %04.3f (cog_cogU_rsesU_mean[1,1]) `star_cog_cogU_rsesU' " & " %02.0f ( n_cog_u_rses_u ) " & " %02.0f ( n_clust_cog_u_rses_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogU_rsesU_pv[1,1]) " ) & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & & RSES Bottom 50\% & " %04.3f (cog_cogU_rsesL_mean[1,1]) `star_cog_cogU_rsesL' " & " %02.0f ( n_cog_u_rses_l ) " & " %02.0f ( n_clust_cog_u_rses_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogU_rsesL_pv[1,1]) " ) & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & Math Bottom 50\%  & RSES Top 50\% & " %04.3f (cog_cogL_rsesU_mean[1,1]) `star_cog_cogL_rsesU' " & " %02.0f ( n_cog_l_rses_u ) " & " %02.0f ( n_clust_cog_l_rses_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogL_rsesU_pv[1,1]) " ) & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & & RSES Bottom 50\% & " %04.3f (cog_cogL_rsesL_mean[1,1]) `star_cog_cogL_rsesL' " & " %02.0f ( n_cog_l_rses_l ) " & " %02.0f ( n_clust_cog_l_rses_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogL_rsesL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. 
. file write `hh2' " RSES score^{c -(}a{c )-} & Math Top 50\%  & RSES Top 50\% & " %04.3f (rses_cogU_rsesU_mean[1,1]) `star_rses_cogU_rsesU' " & " %02.0f ( n_rses_u_cog_u ) " & " %02.0f ( n_clust_rses_u_cog_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (rses_cogU_rsesU_pv[1,1]) " ) & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & & RSES Bottom 50\% & " %04.3f (rses_cogU_rsesL_mean[1,1]) `star_rses_cogU_rsesL' " & " %02.0f ( n_rses_l_cog_u ) " & " %02.0f ( n_clust_rses_l_cog_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &       & & ( " %04.3f (rses_cogU_rsesL_pv[1,1]) " ) & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & Math Bottom 50\%  & RSES Top 50\% & " %04.3f (rses_cogL_rsesU_mean[1,1]) `star_rses_cogL_rsesU' " & " %02.0f ( n_rses_u_cog_l ) " & " %02.0f ( n_clust_rses_u_cog_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (rses_cogL_rsesU_pv[1,1]) " ) & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & & RSES Bottom 50\% & " %04.3f (rses_cogL_rsesL_mean[1,1]) `star_rses_cogL_rsesL' " & " %02.0f ( n_rses_l_cog_l ) " & " %02.0f ( n_clust_rses_l_cog_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &       & & ( " %04.3f (rses_cogL_rsesL_pv[1,1]) " ) & &  \\ " _newline
{txt}
{com}. 
. file write `hh2' "\\\midrule" _newline
{txt}
{com}. file write `hh2' "\end{c -(}tabular{c )-}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\item (a) Dependent variables are standardized using the average and variance of the whole follow-up sample in the February 2022 survey. " _newline
{txt}
{com}. file write `hh2' "\item (b) For the propensity score matching, we use covariates including student's grade, sex, baseline cognitive and baseline non-cognitive score, DT baseline time, branch dummy (location), parents' income source, last income per family member, number of household members, age of household head, education level of household head, teacher's age, sex, and phone survey dummy." _newline
{txt}
{com}. file write `hh2' "\item (c) Cutoffs are created based on whether their ability to perform each item at the baseline was higher or lower than the median. " _newline
{txt}
{com}. file write `hh2' "\item (d) Wild cluster bootstrap p-values are reported within parentheses. Clusters are schools at the baseline." _newline
{txt}
{com}. file write `hh2' "\item (e) $^*$ Significant at 10\% level; $^{c -(}**{c )-}$ significant at 5\% level; $^{c -(}***{c )-}$ significant at 1\% level. " _newline
{txt}
{com}. file write `hh2' "\end{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}threeparttable{c )-}" _newline
{txt}
{com}. file write `hh2' "{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:addlabel{c )-}%" _newline
{txt}
{com}. file write `hh2' "\end{c -(}table{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. file close `hh2'
{txt}
{com}. 
. 
. tempname hh2
{txt}
{com}. file open `hh2' using "$path_output/hetero_2by2_CPCS_matchsample.tex", write replace
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "% Author: Kazuma Takakura" _newline
{txt}
{com}. file write `hh2' "% Date: `c(current_date)'" _newline
{txt}
{com}. file write `hh2' "% Time: `c(current_time)'" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. 
. file write `hh2' "\begin{c -(}table{c )-}[h!]\footnotesize" _newline
{txt}
{com}. file write `hh2' "  \centering" _newline
{txt}
{com}. file write `hh2' "  \caption{c -(}Heterogeneity among Baseline Abilites (Matched Sample, Math and CPCS){c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:hetero_cpcs_match{c )-}" _newline
{txt}
{com}. file write `hh2' "\scalebox{c -(}1{c )-}{c -(}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}threeparttable{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "\begin{c -(}tabular{c )-}{c -(}lccccccc{c )-}\toprule" _newline
{txt}
{com}. 
. file write `hh2' " Dependent Variable & \multicolumn{c -(}2{c )-}{c -(}c{c )-}{c -(}Baseline^{c -(}b{c )-}{c )-} & Difference & Obs & N of clusters  \\\midrule\midrule" _newline
{txt}
{com}. file write `hh2' " Rapid math test score^{c -(}a{c )-} & Math Top 50\%  & CPCS Top 50\% & " %04.3f (cog_cogU_rsesU_mean[1,1]) `star_cog_cogU_cpcsU' " & " %02.0f ( n_cog_u_cpcs_u ) " & " %02.0f ( n_clust_cog_u_cpcs_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogU_cpcsU_pv[1,1]) " )  & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cog_cogU_cpcsL_mean[1,1]) `star_cog_cogU_cpcsL' " & " %02.0f ( n_cog_u_cpcs_l ) " & " %02.0f ( n_clust_cog_u_cpcs_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogU_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & Math Bottom 50\%  & CPCS Top 50\% & " %04.3f (cog_cogL_cpcsU_mean[1,1]) `star_cog_cogL_cpcsU' " & " %02.0f ( n_cog_l_cpcs_u ) " & " %02.0f ( n_clust_cog_l_cpcs_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogL_cpcsU_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cog_cogL_cpcsL_mean[1,1]) `star_cog_cogL_cpcsL' " & " %02.0f ( n_cog_l_cpcs_l ) " & " %02.0f ( n_clust_cog_l_cpcs_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogL_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. 
. file write `hh2' " CPCS score^{c -(}a{c )-} & Math Top 50\%  & CPCS Top 50\% & " %04.3f (cpcs_cogU_cpcsU_mean[1,1]) `star_cpcs_cogU_cpcsU' " & " %02.0f ( n_cpcs_u_cog_u ) " & " %02.0f ( n_clust_cpcs_u_cog_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cpcs_cogU_cpcsU_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cpcs_cogU_cpcsL_mean[1,1]) `star_cpcs_cogU_cpcsL' " & " %02.0f ( n_cpcs_l_cog_u ) " & " %02.0f ( n_clust_cpcs_l_cog_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &       & & ( " %04.3f (cpcs_cogU_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & Math Bottom 50\%  & CPCS Top 50\% & " %04.3f (cpcs_cogL_cpcsU_mean[1,1]) `star_cpcs_cogL_cpcsU' " & " %02.0f ( n_cpcs_u_cog_l ) " & " %02.0f ( n_clust_cpcs_u_cog_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cpcs_cogL_cpcsU_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cpcs_cogL_cpcsL_mean[1,1]) `star_cpcs_cogL_cpcsL' " & " %02.0f ( n_cpcs_l_cog_l ) " & " %02.0f ( n_clust_cpcs_l_cog_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &       & & ( " %04.3f (cpcs_cogL_cpcsL_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. 
. file write `hh2' "\\\midrule" _newline
{txt}
{com}. file write `hh2' "\end{c -(}tabular{c )-}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\item (a) Dependent variables are standardized using the average and variance of the whole follow-up sample in the February 2022 survey. " _newline
{txt}
{com}. file write `hh2' "\item (b) For the propensity score matching, we use covariates including student's grade, sex, baseline cognitive and baseline non-cognitive score, DT baseline time, branch dummy (location), parents' income source, last income per family member, number of household members, age of household head, education level of household head, teacher's age, sex, and phone survey dummy." _newline
{txt}
{com}. file write `hh2' "\item (c) Cutoffs are created based on whether their ability to perform each item at the baseline was higher or lower than the median. " _newline
{txt}
{com}. file write `hh2' "\item (d) Wild cluster bootstrap p-values are reported within parentheses. Clusters are schools at the baseline." _newline
{txt}
{com}. file write `hh2' "\item (e) $^*$ Significant at 10\% level; $^{c -(}**{c )-}$ significant at 5\% level; $^{c -(}***{c )-}$ significant at 1\% level. " _newline
{txt}
{com}. file write `hh2' "\end{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}threeparttable{c )-}" _newline
{txt}
{com}. file write `hh2' "{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:addlabel{c )-}%" _newline
{txt}
{com}. file write `hh2' "\end{c -(}table{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. file close `hh2'
{txt}
{com}. 
{txt}end of do-file

{com}. 
. do "$path_do/3_table_D1.do"
{txt}
{com}. * This is the do file to create "Table D1. Hausman-Wise Test"
. set seed 123
{txt}
{com}. 
. use "$path_data/temp/student_unbalance", clear
{txt}
{com}. 
. gen treatment_missing_dummy = 1 if treatment == .
{txt}(1,005 missing values generated)

{com}. recode treatment_missing_dummy (.=0)
{txt}(1,005 changes made to {bf:treatment_missing_dummy})

{com}. replace treatment = 0 if treatment == .
{txt}(50 real changes made)

{com}. 
. gen DT_score_pre_missing_0 = 0 if DT_score_pre == .
{txt}(968 missing values generated)

{com}. replace DT_score_pre_missing_0 = DT_score_pre if DT_score_pre != .
{txt}(968 real changes made)

{com}. 
. gen rosen_pre_missing_0 = 0 if rosen_pre == .
{txt}(1,011 missing values generated)

{com}. replace rosen_pre_missing_0 = rosen_pre if rosen_pre != .
{txt}(1,011 real changes made)

{com}. 
. gen cpcs_pre_missing_0 = 0 if cpcs_pre == .
{txt}(1,011 missing values generated)

{com}. replace cpcs_pre_missing_0 = cpcs_pre if cpcs_pre != .
{txt}(1,011 real changes made)

{com}. 
. gen DT_score_pre_missing_dummy = 1 if DT_score_pre == .
{txt}(968 missing values generated)

{com}. gen rosen_pre_missing_dummy = 1 if rosen_pre == .
{txt}(1,011 missing values generated)

{com}. gen cpcs_pre_missing_dummy = 1 if cpcs_pre == .
{txt}(1,011 missing values generated)

{com}. recode DT_score_pre_missing_dummy rosen_pre_missing_dummy cpcs_pre_missing_dummy (.=0)
{txt}(968 changes made to {bf:DT_score_pre_missing_dummy})
(1,011 changes made to {bf:rosen_pre_missing_dummy})
(1,011 changes made to {bf:cpcs_pre_missing_dummy})

{com}. 
. gen grade_missing_0 = 0 if grade == .
{txt}(1,005 missing values generated)

{com}. replace grade_missing_0 = grade if grade != .
{txt}(1,005 real changes made)

{com}. gen student_gender_missing_0 = 0 if student_gender == .
{txt}(978 missing values generated)

{com}. replace student_gender_missing_0 = grade if student_gender != .
{txt}(978 real changes made)

{com}. gen grade_missing_dummy = 1 if grade == .
{txt}(1,005 missing values generated)

{com}. gen student_gender_missing_dummy = 1 if student_gender == .
{txt}(978 missing values generated)

{com}. recode grade_missing_dummy student_gender_missing_dummy (.=0)
{txt}(1,005 changes made to {bf:grade_missing_dummy})
(978 changes made to {bf:student_gender_missing_dummy})

{com}. 
. *** full sample
. wildbootstrap reg attrition treatment treatment_missing_dummy, cluster(school_no) reps(1000)
{txt}{p 0 6 2}note: {bf:treatment_missing_dummy} omitted because of collinearity.{p_end}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 52}{txt}Number of obs{col 70} = {res}1,005
{txt}Linear regression{col 52}{txt}Number of clusters{col 70} = {res}   34
{col 52}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 67}{txt}min{col 70} = {res}   16
{txt}Error weight: Rademacher{col 67}{txt}avg{col 70} = {res} 29.6
{col 67}{txt}max{col 70} = {res}   38
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}               attrition{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0701214{col 38}{space 1}  -1.75{col 46}{space 3}0.094{col 54}{space 3}-.1508893{col 66}{space 3} .0141631
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. mat hausman_simple = r(table)
{txt}
{com}. scalar n_hausman_simple = e(N)
{txt}
{com}. mean attrition if treatment == 0
{res}
{txt}{col 1}Mean estimation{col 44}{lalign 13:Number of obs}{col 57} = {res}{ralign 3:528}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}       Mean{col 26}   Std. err.{col 38}     [95% con{col 51}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 3}attrition {c |}{col 14}{res}{space 2} .8143939{col 26}{space 2} .0169359{col 37}{space 5} .7811238{col 51}{space 3} .8476641
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. matrix mean_hausman_simple = e(b)
{txt}
{com}. 
. wildbootstrap reg attrition treatment DT_score_pre_missing_0 rosen_pre_missing_0 cpcs_pre_missing_0 DT_score_pre_missing_dummy rosen_pre_missing_dummy cpcs_pre_missing_dummy i.grade_missing_0 student_gender_missing_0 grade_missing_dummy student_gender_missing_dummy treatment_missing_dummy, cluster(school_no) reps(1000)
{txt}{p 0 6 2}note: {bf:rosen_pre_missing_dummy} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:cpcs_pre_missing_dummy} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:grade_missing_dummy} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:treatment_missing_dummy} omitted because of collinearity.{p_end}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:29})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:DT_score_pre_missing_0 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:DT_score_pre_missing_0}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:rosen_pre_missing_0 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:rosen_pre_missing_0}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:cpcs_pre_missing_0 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:cpcs_pre_missing_0}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:DT_score_pre_missing_dummy = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:DT_score_pre_missing_dummy}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}30{text}.{text} done{text} ({result:31})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:4.grade_missing_0 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:4.grade_missing_0}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:student_gender_missing_0 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:student_gender_missing_0}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:student_gender_missing_dummy = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:student_gender_missing_dummy}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 52}{txt}Number of obs{col 70} = {res}1,005
{txt}Linear regression{col 52}{txt}Number of clusters{col 70} = {res}   34
{col 52}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 67}{txt}min{col 70} = {res}   16
{txt}Error weight: Rademacher{col 67}{txt}avg{col 70} = {res} 29.6
{col 67}{txt}max{col 70} = {res}   38
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}               attrition{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraints             {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0701666{col 38}{space 1}  -1.89{col 46}{space 3}0.084{col 54}{space 3}-.1507906{col 66}{space 3} .0106962
{col 1}{text} DT_score_pre_missing_0 {col 26}{c |}
{res}{col 1}{text}                     = 0{col 26}{c |}{result}{space 2}-.0424797{col 38}{space 1}  -3.00{col 46}{space 3}0.010{col 54}{space 3}-.0724186{col 66}{space 3}-.0102347
{col 26}{text}{c |}
{res}{col 1}{text} rosen_pre_missing_0 = 0{col 26}{c |}{result}{space 2}-.0106981{col 38}{space 1}  -0.79{col 46}{space 3}0.446{col 54}{space 3}-.0377735{col 66}{space 3} .0188643
{col 1}{text}  cpcs_pre_missing_0 = 0{col 26}{c |}{result}{space 2} .0126426{col 38}{space 1}   1.22{col 46}{space 3}0.222{col 54}{space 3}-.0082406{col 66}{space 3} .0340754
{col 1}{text}DT_score_pre_missing_dum{col 26}{c |}
{res}{col 1}{text}                  my = 0{col 26}{c |}{result}{space 2} .0234753{col 38}{space 1}   0.30{col 46}{space 3}0.780{col 54}{space 3}-.2733793{col 66}{space 3} .2783458
{col 26}{text}{c |}
{res}{col 1}{text}   4.grade_missing_0 = 0{col 26}{c |}{result}{space 2} .1238316{col 38}{space 1}   1.40{col 46}{space 3}0.162{col 54}{space 3}-.0597666{col 66}{space 3} .3443366
{col 1}{text}student_gender_missing_0{col 26}{c |}
{res}{col 1}{text}                     = 0{col 26}{c |}{result}{space 2}-.0370784{col 38}{space 1}  -0.78{col 46}{space 3}0.414{col 54}{space 3}-.1646139{col 66}{space 3} .0723878
{col 26}{text}{c |}
{res}{col 1}{text}student_gender_missing_d{col 26}{c |}
{res}{col 1}{text}                ummy = 0{col 26}{c |}{result}{space 2}   .04188{col 38}{space 1}   0.21{col 46}{space 3}0.818{col 54}{space 3}-.6498151{col 66}{space 3} .4134509
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. mat hausman_control = r(table)
{txt}
{com}. scalar n_hausman_control = e(N)
{txt}
{com}. 
. *** subsample
. egen DT_score_pre_med = median(DT_score_pre)
{txt}
{com}. gen DT_score_pre_upper50 = 1 if DT_score_pre>DT_score_pre_med
{txt}(491 missing values generated)

{com}. recode DT_score_pre_upper50 (.=0)
{txt}(491 changes made to {bf:DT_score_pre_upper50})

{com}. 
. wildbootstrap reg attrition treatment DT_score_pre rosen_pre_missing_0 cpcs_pre_missing_0 rosen_pre_missing_dummy cpcs_pre_missing_dummy i.grade_missing_0 student_gender_missing_0 grade_missing_dummy student_gender_missing_dummy treatment_missing_dummy if DT_score_pre_upper50 == 1, cluster(school_no) reps(1000)
{txt}{p 0 6 2}note: {bf:rosen_pre_missing_dummy} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:cpcs_pre_missing_dummy} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:grade_missing_dummy} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:treatment_missing_dummy} omitted because of collinearity.{p_end}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:DT_score_pre = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:DT_score_pre}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:rosen_pre_missing_0 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:rosen_pre_missing_0}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:cpcs_pre_missing_0 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:cpcs_pre_missing_0}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:4.grade_missing_0 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:4.grade_missing_0}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}30{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}40{text}.{text}.{text}.{text}.{text} done{text} ({result:44})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}30{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:39})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:student_gender_missing_0 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:student_gender_missing_0}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}30{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:38})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}30{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:38})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:student_gender_missing_dummy = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:student_gender_missing_dummy}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}30{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:39})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}30{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:39})
{res}
{txt}Wild cluster bootstrap{col 53}{txt}Number of obs{col 71} = {res} 477
{txt}Linear regression{col 53}{txt}Number of clusters{col 71} = {res}  34
{col 53}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 68}{txt}min{col 71} = {res}   2
{txt}Error weight: Rademacher{col 68}{txt}avg{col 71} = {res}14.0
{col 68}{txt}max{col 71} = {res}  29
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}               attrition{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraints             {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0647459{col 38}{space 1}  -1.44{col 46}{space 3}0.156{col 54}{space 3}-.1637955{col 66}{space 3} .0338193
{col 1}{text}        DT_score_pre = 0{col 26}{c |}{result}{space 2}-.1086732{col 38}{space 1}  -1.80{col 46}{space 3}0.098{col 54}{space 3} -.237448{col 66}{space 3} .0195303
{col 1}{text} rosen_pre_missing_0 = 0{col 26}{c |}{result}{space 2} .0049081{col 38}{space 1}   0.22{col 46}{space 3}0.802{col 54}{space 3}-.0430353{col 66}{space 3} .0523021
{col 1}{text}  cpcs_pre_missing_0 = 0{col 26}{c |}{result}{space 2} .0024088{col 38}{space 1}   0.12{col 46}{space 3}0.944{col 54}{space 3}-.0409289{col 66}{space 3} .0495796
{col 1}{text}   4.grade_missing_0 = 0{col 26}{c |}{result}{space 2}-.0021494{col 38}{space 1}  -0.05{col 46}{space 3}0.938{col 54}{space 3}-20.77565{col 66}{space 3} 15.35347
{col 1}{text}student_gender_missing_0{col 26}{c |}
{res}{col 1}{text}                     = 0{col 26}{c |}{result}{space 2} .0101931{col 38}{space 1}   0.34{col 46}{space 3}0.704{col 54}{space 3}-7.103864{col 66}{space 3} 8.356383
{col 26}{text}{c |}
{res}{col 1}{text}student_gender_missing_d{col 26}{c |}
{res}{col 1}{text}                ummy = 0{col 26}{c |}{result}{space 2} .3697013{col 38}{space 1}   4.31{col 46}{space 3}0.238{col 54}{space 3}-45.31659{col 66}{space 3} 36.33405
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. mat hausman_upper = r(table)
{txt}
{com}. scalar n_hausman_upper = e(N)
{txt}
{com}. mean attrition if treatment == 0 & DT_score_pre_upper50 == 1
{res}
{txt}{col 1}Mean estimation{col 44}{lalign 13:Number of obs}{col 57} = {res}{ralign 3:305}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}       Mean{col 26}   Std. err.{col 38}     [95% con{col 51}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 3}attrition {c |}{col 14}{res}{space 2} .8065574{col 26}{space 2} .0226546{col 37}{space 5} .7619776{col 51}{space 3} .8511371
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. matrix mean_hausman_upper = e(b)
{txt}
{com}. 
. 
. wildbootstrap reg attrition treatment DT_score_pre rosen_pre_missing_0 cpcs_pre_missing_0 rosen_pre_missing_dummy cpcs_pre_missing_dummy i.grade_missing_0 student_gender_missing_0 grade_missing_dummy student_gender_missing_dummy treatment_missing_dummy if DT_score_pre_upper50 == 0, cluster(school_no) reps(1000)
{txt}{p 0 6 2}note: {bf:rosen_pre_missing_dummy} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:cpcs_pre_missing_dummy} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:student_gender_missing_0} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:grade_missing_dummy} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:student_gender_missing_dummy} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:treatment_missing_dummy} omitted because of collinearity.{p_end}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:DT_score_pre = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:DT_score_pre}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:rosen_pre_missing_0 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:rosen_pre_missing_0}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:cpcs_pre_missing_0 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:cpcs_pre_missing_0}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:29})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:4.grade_missing_0 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:4.grade_missing_0}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 53}{txt}Number of obs{col 71} = {res} 491
{txt}Linear regression{col 53}{txt}Number of clusters{col 71} = {res}  34
{col 53}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 68}{txt}min{col 71} = {res}   3
{txt}Error weight: Rademacher{col 68}{txt}avg{col 71} = {res}14.4
{col 68}{txt}max{col 71} = {res}  28
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}               attrition{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraints             {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} -.085646{col 38}{space 1}  -1.71{col 46}{space 3}0.106{col 54}{space 3}-.1860613{col 66}{space 3} .0182412
{col 1}{text}        DT_score_pre = 0{col 26}{c |}{result}{space 2}-.0255488{col 38}{space 1}  -1.28{col 46}{space 3}0.206{col 54}{space 3}-.0704921{col 66}{space 3} .0124918
{col 1}{text} rosen_pre_missing_0 = 0{col 26}{c |}{result}{space 2}-.0252415{col 38}{space 1}  -1.77{col 46}{space 3}0.074{col 54}{space 3}-.0574799{col 66}{space 3} .0022561
{col 1}{text}  cpcs_pre_missing_0 = 0{col 26}{c |}{result}{space 2} .0230144{col 38}{space 1}   1.90{col 46}{space 3}0.068{col 54}{space 3}-.0019404{col 66}{space 3}  .049043
{col 1}{text}   4.grade_missing_0 = 0{col 26}{c |}{result}{space 2} .0837875{col 38}{space 1}   1.81{col 46}{space 3}0.086{col 54}{space 3}-.0156534{col 66}{space 3} .1828532
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. mat hausman_lower = r(table)
{txt}
{com}. scalar n_hausman_lower = e(N)
{txt}
{com}. mean attrition if treatment == 0 & DT_score_pre_upper50 == 0
{res}
{txt}{col 1}Mean estimation{col 44}{lalign 13:Number of obs}{col 57} = {res}{ralign 3:223}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}       Mean{col 26}   Std. err.{col 38}     [95% con{col 51}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 3}attrition {c |}{col 14}{res}{space 2} .8251121{col 26}{space 2} .0254953{col 37}{space 5} .7748684{col 51}{space 3} .8753559
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. matrix mean_hausman_lower = e(b)
{txt}
{com}. 
. wildbootstrap reg attrition treatment DT_score_pre_missing_0 rosen_pre_missing_0 cpcs_pre_missing_0 DT_score_pre_missing_dummy rosen_pre_missing_dummy cpcs_pre_missing_dummy student_gender_missing_0 student_gender_missing_dummy treatment_missing_dummy if grade == 4, cluster(school_no) reps(1000)
{txt}{p 0 6 2}note: {bf:rosen_pre_missing_dummy} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:cpcs_pre_missing_dummy} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:student_gender_missing_dummy} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:treatment_missing_dummy} omitted because of collinearity.{p_end}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:DT_score_pre_missing_0 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:DT_score_pre_missing_0}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{p 0 6 2}{txt}note: lower-bound CI achieved F({bf:-0.07}) = {bf: 0.0240}, but target is F(x) = {bf:.025}.{p_end}
{txt}{p 0 6 2}note: the sorted bootstrap t statistics have at least two tied values adjacent to the t statistic under the null; this prevents the CI bound from converging to the target.{p_end}
{txt}{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:rosen_pre_missing_0 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:rosen_pre_missing_0}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:cpcs_pre_missing_0 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:cpcs_pre_missing_0}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:DT_score_pre_missing_dummy = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:DT_score_pre_missing_dummy}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:student_gender_missing_0 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:student_gender_missing_0}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}30{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:35})
{res}
{txt}Wild cluster bootstrap{col 53}{txt}Number of obs{col 71} = {res} 422
{txt}Linear regression{col 53}{txt}Number of clusters{col 71} = {res}  15
{col 53}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 68}{txt}min{col 71} = {res}  16
{txt}Error weight: Rademacher{col 68}{txt}avg{col 71} = {res}28.1
{col 68}{txt}max{col 71} = {res}  34
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}               attrition{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraints             {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.1216409{col 38}{space 1}  -2.57{col 46}{space 3}0.036{col 54}{space 3}-.2207466{col 66}{space 3}-.0123294
{col 1}{text} DT_score_pre_missing_0 {col 26}{c |}
{res}{col 1}{text}                     = 0{col 26}{c |}{result}{space 2}-.0395291{col 38}{space 1}  -2.48{col 46}{space 3}0.026{col 54}{space 3}-.0734011{col 66}{space 3}-.0051238
{col 26}{text}{c |}
{res}{col 1}{text} rosen_pre_missing_0 = 0{col 26}{c |}{result}{space 2}-.0221403{col 38}{space 1}  -1.34{col 46}{space 3}0.220{col 54}{space 3}-.0557807{col 66}{space 3} .0133443
{col 1}{text}  cpcs_pre_missing_0 = 0{col 26}{c |}{result}{space 2} .0147711{col 38}{space 1}   1.31{col 46}{space 3}0.228{col 54}{space 3}-.0122483{col 66}{space 3}  .039106
{col 1}{text}DT_score_pre_missing_dum{col 26}{c |}
{res}{col 1}{text}                  my = 0{col 26}{c |}{result}{space 2}-.0608206{col 38}{space 1}  -0.71{col 46}{space 3}0.452{col 54}{space 3}-.6979156{col 66}{space 3} .2692559
{col 26}{text}{c |}
{res}{col 1}{text}student_gender_missing_0{col 26}{c |}
{res}{col 1}{text}                     = 0{col 26}{c |}{result}{space 2}-.0642315{col 38}{space 1}  -3.10{col 46}{space 3}0.024{col 54}{space 3}-.1301898{col 66}{space 3}-.0306019
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. mat hausman_grade4 = r(table)
{txt}
{com}. scalar n_hausman_grade4 = e(N)
{txt}
{com}. mean attrition if treatment == 0 & grade == 4
{res}
{txt}{col 1}Mean estimation{col 44}{lalign 13:Number of obs}{col 57} = {res}{ralign 3:210}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}       Mean{col 26}   Std. err.{col 38}     [95% con{col 51}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 3}attrition {c |}{col 14}{res}{space 2} .8428571{col 26}{space 2} .0251739{col 37}{space 5} .7932298{col 51}{space 3} .8924845
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. matrix mean_hausman_grade4 = e(b)
{txt}
{com}. 
. 
. wildbootstrap reg attrition treatment DT_score_pre_missing_0 rosen_pre_missing_0 cpcs_pre_missing_0 DT_score_pre_missing_dummy rosen_pre_missing_dummy cpcs_pre_missing_dummy student_gender_missing_0 student_gender_missing_dummy treatment_missing_dummy if grade == 2, cluster(school_no) reps(1000)
{txt}{p 0 6 2}note: {bf:rosen_pre_missing_dummy} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:cpcs_pre_missing_dummy} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:student_gender_missing_dummy} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:treatment_missing_dummy} omitted because of collinearity.{p_end}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:DT_score_pre_missing_0 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:DT_score_pre_missing_0}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}30{text} done{text} ({result:30})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:rosen_pre_missing_0 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:rosen_pre_missing_0}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:cpcs_pre_missing_0 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:cpcs_pre_missing_0}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:DT_score_pre_missing_dummy = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:DT_score_pre_missing_dummy}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}30{text}.{text}.{text} done{text} ({result:32})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}30{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:39})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:student_gender_missing_0 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:student_gender_missing_0}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}30{text} done{text} ({result:30})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:29})
{res}
{txt}Wild cluster bootstrap{col 53}{txt}Number of obs{col 71} = {res} 583
{txt}Linear regression{col 53}{txt}Number of clusters{col 71} = {res}  19
{col 53}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 68}{txt}min{col 71} = {res}  27
{txt}Error weight: Rademacher{col 68}{txt}avg{col 71} = {res}30.7
{col 68}{txt}max{col 71} = {res}  38
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}               attrition{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraints             {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0325048{col 38}{space 1}  -0.65{col 46}{space 3}0.594{col 54}{space 3}-.1352183{col 66}{space 3} .0777536
{col 1}{text} DT_score_pre_missing_0 {col 26}{c |}
{res}{col 1}{text}                     = 0{col 26}{c |}{result}{space 2}-.0470574{col 38}{space 1}  -2.25{col 46}{space 3}0.040{col 54}{space 3}-.0953264{col 66}{space 3}-.0021292
{col 26}{text}{c |}
{res}{col 1}{text} rosen_pre_missing_0 = 0{col 26}{c |}{result}{space 2}  .001101{col 38}{space 1}   0.06{col 46}{space 3}0.986{col 54}{space 3}-.0446805{col 66}{space 3} .0487595
{col 1}{text}  cpcs_pre_missing_0 = 0{col 26}{c |}{result}{space 2} .0103347{col 38}{space 1}   0.61{col 46}{space 3}0.554{col 54}{space 3}-.0236239{col 66}{space 3} .0480546
{col 1}{text}DT_score_pre_missing_dum{col 26}{c |}
{res}{col 1}{text}                  my = 0{col 26}{c |}{result}{space 2} .1514967{col 38}{space 1}   1.41{col 46}{space 3}0.394{col 54}{space 3}-2.862466{col 66}{space 3} 14.00384
{col 26}{text}{c |}
{res}{col 1}{text}student_gender_missing_0{col 26}{c |}
{res}{col 1}{text}                     = 0{col 26}{c |}{result}{space 2}-.0063052{col 38}{space 1}  -0.09{col 46}{space 3}0.948{col 54}{space 3}-1.427045{col 66}{space 3} .5428899
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. mat hausman_grade3 = r(table)
{txt}
{com}. scalar n_hausman_grade3 = e(N)
{txt}
{com}. mean attrition if treatment == 0 & grade == 2
{res}
{txt}{col 1}Mean estimation{col 44}{lalign 13:Number of obs}{col 57} = {res}{ralign 3:268}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}       Mean{col 26}   Std. err.{col 38}     [95% con{col 51}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 3}attrition {c |}{col 14}{res}{space 2} .7574627{col 26}{space 2}  .026231{col 37}{space 5} .7058168{col 51}{space 3} .8091085
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. matrix mean_hausman_grade3 = e(b)
{txt}
{com}. 
. 
. local specification simple control upper lower grade4 grade3
{txt}
{com}. foreach spec in `specification'{c -(}
{txt}  2{com}.                 if hausman_`spec'[3,1]<=0.01 {c -(}
{txt}  3{com}.                         local star_`spec' %3s "***"
{txt}  4{com}.                 {c )-}
{txt}  5{com}.                 else if (hausman_`spec'[3,1]>0.01) & (hausman_`spec'[3,1]<=0.05) {c -(}
{txt}  6{com}.                         local star_`spec' %2s "**"
{txt}  7{com}.                 {c )-}
{txt}  8{com}.                 else if (hausman_`spec'[3,1]>0.05) & (hausman_`spec'[3,1]<=0.10) {c -(}
{txt}  9{com}.                         local star_`spec' %1s "*"
{txt} 10{com}.                 {c )-}
{txt} 11{com}.                 else {c -(}
{txt} 12{com}.                         local star_`spec'  ""
{txt} 13{com}.                 {c )-}
{txt} 14{com}. {c )-}
{txt}
{com}. 
. /// Table
> 
. tempname hh2
{txt}
{com}. file open `hh2' using "$path_output/hausman_test.tex", write replace
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "% Author: Kazuma Takakura" _newline
{txt}
{com}. file write `hh2' "% Date: `c(current_date)'" _newline
{txt}
{com}. file write `hh2' "% Time: `c(current_time)'" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. 
. file write `hh2' "\begin{c -(}table{c )-}[h!]\footnotesize" _newline
{txt}
{com}. file write `hh2' "  \centering" _newline
{txt}
{com}. file write `hh2' "  \caption{c -(}Hausman-Wise Test{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:hausman{c )-}" _newline
{txt}
{com}. file write `hh2' "\scalebox{c -(}0.7{c )-}{c -(}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}threeparttable{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "\begin{c -(}tabular{c )-}{c -(}lcccccc{c )-}\toprule" _newline
{txt}
{com}. 
. 
. file write `hh2' "  & Full sample & Full sample & DT Score $>$ median & DT Score $\leq$ median & Grade 4 & Grade 3 \\" _newline
{txt}
{com}. 
. file write `hh2' "  Treatment & " %04.3f (hausman_simple[1,1]) `star_simple' "  & " %04.3f (hausman_control[1,1]) `star_control' " & " %04.3f (hausman_upper[1,1]) `star_upper' " & " %04.3f (hausman_lower[1,1]) `star_lower' " & " %04.3f (hausman_grade4[1,1]) `star_grade4' " & " %04.3f (hausman_grade3[1,1]) `star_grade3' " \\ " _newline
{txt}
{com}. file write `hh2' "    & (" %04.3f (hausman_simple[3,1]) ") & (" %04.3f (hausman_control[3,1]) ") & (" %04.3f (hausman_upper[3,1]) ") & (" %04.3f (hausman_lower[3,1]) ") & (" %04.3f (hausman_grade4[3,1]) ") & (" %04.3f (hausman_grade3[3,1]) ") \\ " _newline
{txt}
{com}. file write `hh2' "  Control Mean & " %04.3f (mean_hausman_simple[1,1]) "  & " %04.3f (mean_hausman_simple[1,1]) " & " %04.3f (mean_hausman_upper[1,1]) " & " %04.3f (mean_hausman_lower[1,1]) " & " %04.3f (mean_hausman_grade4[1,1]) " & " %04.3f (mean_hausman_grade3[1,1])  " \\ " _newline
{txt}
{com}. 
. file write `hh2' "  Control & N & Y & Y & Y & Y & Y \\ " _newline
{txt}
{com}. file write `hh2' "  Observations & " (n_hausman_simple) "  & " (n_hausman_control) " & " (n_hausman_upper) " & " (n_hausman_lower) "  & " (n_hausman_grade4) " & " (n_hausman_grade3) " \\ " _newline
{txt}
{com}. 
. 
. file write `hh2' "\midrule" _newline
{txt}
{com}. file write `hh2' "\end{c -(}tabular{c )-}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\item (a) Dependent variable is the attrition dummy." _newline
{txt}
{com}. file write `hh2' "\item (b) Controls are the students' grade, sex, baseline cognitive and baseline non-cognitive scores." _newline
{txt}
{com}. file write `hh2' "\item (c) Wild clustered bootstrap p-values are reported within parentheses. Clusters are schools at the baseline. There are 34 clusters. " _newline
{txt}
{com}. file write `hh2' "\item (d) $^*$ Significant at 10\% level; $^{c -(}**{c )-}$ significant at 5\% level; $^{c -(}***{c )-}$ significant at 1\% level. " _newline
{txt}
{com}. file write `hh2' "\end{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}threeparttable{c )-}" _newline
{txt}
{com}. file write `hh2' "{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}table{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. file close `hh2'
{txt}
{com}. 
{txt}end of do-file

{com}. 
. do "$path_do/3_table_D2_D3.do"
{txt}
{com}. * This is the do file to create "Table D2. Summary Statistics by Attrition Status" and "Table D3. Summary Statistics by Attrition Status and Treatment Status"
. set seed 123
{txt}
{com}. 
. use "$path_data/temp/student_unbalance", clear
{txt}
{com}. 
. gen female = 1 if student_gender == 0
{txt}(468 missing values generated)

{com}. gen grade_2 = 1 if grade == 2
{txt}(472 missing values generated)

{com}. recode female grade_2(.=0)
{txt}(468 changes made to {bf:female})
(472 changes made to {bf:grade_2})

{com}. 
. gen tracked = 1 - attrition
{txt}
{com}. 
. /// Standardization
> egen DT_score_pre_mean = mean(DT_score_pre)
{txt}
{com}. egen DT_score_pre_sd = sd(DT_score_pre)
{txt}
{com}. gen DT_score_pre_std = (DT_score_pre-DT_score_pre_mean)/DT_score_pre_sd
{txt}(87 missing values generated)

{com}. drop DT_score_pre_mean DT_score_pre_sd 
{txt}
{com}. 
. egen cpcs_pre_mean = mean(cpcs_pre)
{txt}
{com}. egen cpcs_pre_sd = sd(cpcs_pre)
{txt}
{com}. gen cpcs_pre_std = (cpcs_pre-cpcs_pre_mean)/cpcs_pre_sd
{txt}(44 missing values generated)

{com}. drop cpcs_pre_mean cpcs_pre_sd 
{txt}
{com}. 
. egen rosen_pre_mean = mean(rosen_pre)
{txt}
{com}. egen rosen_pre_sd = sd(rosen_pre)
{txt}
{com}. gen rosen_pre_std = (rosen_pre-rosen_pre_mean)/rosen_pre_sd
{txt}(44 missing values generated)

{com}. drop rosen_pre_mean rosen_pre_sd 
{txt}
{com}. 
. 
. replace school_no = 999 if school_no ==.
{txt}variable {bf}{res}school_no{sf}{txt} was {bf}{res}byte{sf}{txt} now {bf}{res}int{sf}
{txt}(50 real changes made)

{com}. 
. 
. /// Mean difference
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat DT_score_pre_std rosen_pre_std cpcs_pre_std female grade_2 if tracked == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_bl = r(StatTotal)
{txt}  5{com}. 
. tabstat DT_score_pre_std rosen_pre_std cpcs_pre_std female grade_2 if tracked == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_bl = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d    female   grade_2
{hline 9}{c +}{hline 50}
{ralign 8:N} {...}
{c |}{...}
 {res}      239       243       243       243       243
{txt}{hline 9}{c BT}{hline 50}
{res}
{txt}r(StatTotal)[1,5]
   DT_score_p~d  rosen_pre_~d  cpcs_pre_std        female       grade_2
N {res}          239           243           243           243           243
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d    female   grade_2
{hline 9}{c +}{hline 50}
{ralign 8:N} {...}
{c |}{...}
 {res}      729       768       768       812       812
{txt}{hline 9}{c BT}{hline 50}
{res}
{txt}r(StatTotal)[1,5]
   DT_score_p~d  rosen_pre_~d  cpcs_pre_std        female       grade_2
N {res}          729           768           768           812           812
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d    female   grade_2
{hline 9}{c +}{hline 50}
{ralign 8:Mean} {...}
{c |}{...}
 {res}  .161411 -.0237794 -.0322924  .6090535  .6337449
{txt}{hline 9}{c BT}{hline 50}
{res}
{txt}r(StatTotal)[1,5]
      DT_score_p~d  rosen_pre_~d  cpcs_pre_std        female       grade_2
Mean {res}      .161411    -.02377942    -.03229236      .6090535     .63374486
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d    female   grade_2
{hline 9}{c +}{hline 50}
{ralign 8:Mean} {...}
{c |}{...}
 {res} -.052918   .007524  .0102177  .5406404  .5283251
{txt}{hline 9}{c BT}{hline 50}
{res}
{txt}r(StatTotal)[1,5]
      DT_score_p~d  rosen_pre_~d  cpcs_pre_std        female       grade_2
Mean {res}   -.05291801     .00752404     .01021774     .54064039     .52832512
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d    female   grade_2
{hline 9}{c +}{hline 50}
{ralign 8:SD} {...}
{c |}{...}
 {res} .9500507   1.00663  1.047749  .4889696  .4827747
{txt}{hline 9}{c BT}{hline 50}
{res}
{txt}r(StatTotal)[1,5]
    DT_score_p~d  rosen_pre_~d  cpcs_pre_std        female       grade_2
SD {res}    .95005066     1.0066299      1.047749     .48896958     .48277475
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d    female   grade_2
{hline 9}{c +}{hline 50}
{ralign 8:SD} {...}
{c |}{...}
 {res} 1.010871   .998434  .9848952  .4986528  .4995047
{txt}{hline 9}{c BT}{hline 50}
{res}
{txt}r(StatTotal)[1,5]
    DT_score_p~d  rosen_pre_~d  cpcs_pre_std        female       grade_2
SD {res}    1.0108708     .99843399     .98489517     .49865277     .49950471
{reset}
{com}. 
. matrix n_bl = J(1,5,.)
{txt}
{com}. forvalues i = 1/5 {c -(}
{txt}  2{com}.         matrix n_bl[1,`i'] = n_tr_bl[1,`i'] + n_ct_bl[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in DT_score_pre_std rosen_pre_std cpcs_pre_std female grade_2{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' tracked, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:tracked = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:tracked}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 53}{txt}Number of obs{col 71} = {res} 968
{txt}Linear regression{col 53}{txt}Number of clusters{col 71} = {res}  34
{col 53}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 68}{txt}min{col 71} = {res}  15
{txt}Error weight: Rademacher{col 68}{txt}avg{col 71} = {res}28.5
{col 68}{txt}max{col 71} = {res}  38
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        DT_score_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}             tracked = 0{col 26}{c |}{result}{space 2}  .214329{col 38}{space 1}   2.70{col 46}{space 3}0.010{col 54}{space 3}  .049365{col 66}{space 3} .3789084
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:tracked = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:tracked}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:29})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 52}{txt}Number of obs{col 70} = {res}1,011
{txt}Linear regression{col 52}{txt}Number of clusters{col 70} = {res}   35
{col 52}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 67}{txt}min{col 70} = {res}    6
{txt}Error weight: Rademacher{col 67}{txt}avg{col 70} = {res} 28.9
{col 67}{txt}max{col 70} = {res}   38
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}           rosen_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}             tracked = 0{col 26}{c |}{result}{space 2}-.0313035{col 38}{space 1}  -0.34{col 46}{space 3}0.732{col 54}{space 3}-.2083883{col 66}{space 3}  .151691
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:tracked = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:tracked}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 52}{txt}Number of obs{col 70} = {res}1,011
{txt}Linear regression{col 52}{txt}Number of clusters{col 70} = {res}   35
{col 52}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 67}{txt}min{col 70} = {res}    6
{txt}Error weight: Rademacher{col 67}{txt}avg{col 70} = {res} 28.9
{col 67}{txt}max{col 70} = {res}   38
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}            cpcs_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}             tracked = 0{col 26}{c |}{result}{space 2}-.0425101{col 38}{space 1}  -0.49{col 46}{space 3}0.652{col 54}{space 3}-.2120652{col 66}{space 3} .1435618
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:tracked = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:tracked}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 52}{txt}Number of obs{col 70} = {res}1,055
{txt}Linear regression{col 52}{txt}Number of clusters{col 70} = {res}   35
{col 52}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 67}{txt}min{col 70} = {res}   16
{txt}Error weight: Rademacher{col 67}{txt}avg{col 70} = {res} 30.1
{col 67}{txt}max{col 70} = {res}   50
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                  female{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}             tracked = 0{col 26}{c |}{result}{space 2} .0684131{col 38}{space 1}   1.46{col 46}{space 3}0.168{col 54}{space 3}-.0219323{col 66}{space 3} .1641204
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:tracked = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:tracked}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 52}{txt}Number of obs{col 70} = {res}1,055
{txt}Linear regression{col 52}{txt}Number of clusters{col 70} = {res}   35
{col 52}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 67}{txt}min{col 70} = {res}   16
{txt}Error weight: Rademacher{col 67}{txt}avg{col 70} = {res} 30.1
{col 67}{txt}max{col 70} = {res}   50
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                 grade_2{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}             tracked = 0{col 26}{c |}{result}{space 2} .1054197{col 38}{space 1}   1.69{col 46}{space 3}0.090{col 54}{space 3}-.0178682{col 66}{space 3} .2308928
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. 
. // significant level
. 
. local outcome DT_score_pre_std rosen_pre_std cpcs_pre_std female grade_2
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}.                 if r2_`dep'_pv[1,1]<=0.01 {c -(}
{txt}  3{com}.                         local star_`dep' %3s "***"
{txt}  4{com}.                 {c )-}
{txt}  5{com}.                 else if (r2_`dep'_pv[1,1]>0.01) & (r2_`dep'_pv[1,1]<=0.05) {c -(}
{txt}  6{com}.                         local star_`dep' %2s "**"
{txt}  7{com}.                 {c )-}
{txt}  8{com}.                 else if (r2_`dep'_pv[1,1]>0.05) & (r2_`dep'_pv[1,1]<=0.10) {c -(}
{txt}  9{com}.                         local star_`dep' %1s "*"
{txt} 10{com}.                 {c )-}
{txt} 11{com}.                 else {c -(}
{txt} 12{com}.                         local star_`dep'  ""
{txt} 13{com}.                 {c )-}
{txt} 14{com}. {c )-} 
{txt}
{com}. 
. rwolf DT_score_pre_std rosen_pre_std cpcs_pre_std female grade_2, indepvar(attrition) reps(1000) cluster(school_no) vce(cluster school_no)
Bootstrap replications (1000). This may take some time.
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Romano-Wolf step-down adjusted p-values


Independent variable:  attrition
Outcome variables:   DT_score_pre_std rosen_pre_std cpcs_pre_std female grade_2
Number of resamples: 1000


{hline 78}
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
{hline 20}+{hline 57}
   {txt}DT_score_pre_std {c |}    {res}0.0107             0.0120              0.0769
      {txt}rosen_pre_std {c |}    {res}0.7370             0.7353              0.7572
       {txt}cpcs_pre_std {c |}    {res}0.6292             0.6264              0.7572
             {txt}female {c |}    {res}0.1530             0.1808              0.3556
            {txt}grade_2 {c |}    {res}0.1005             0.1179              0.3367
{hline 78}
{txt}
{com}. scalar rwolf_p1 = e(rw_DT_score_pre_std)
{txt}
{com}. scalar rwolf_p2 = e(rw_rosen_pre_std)
{txt}
{com}. scalar rwolf_p3 = e(rw_cpcs_pre_std)
{txt}
{com}. scalar rwolf_p4 = e(rw_female)
{txt}
{com}. scalar rwolf_p5 = e(rw_grade_2)
{txt}
{com}. 
. 
. /// Table
> tempname hh2
{txt}
{com}. file open `hh2' using "$path_output/summary_stat_baseline_attrition.tex", write replace
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "% Author: Kazuma Takakura" _newline
{txt}
{com}. file write `hh2' "% Date: `c(current_date)'" _newline
{txt}
{com}. file write `hh2' "% Time: `c(current_time)'" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. 
. file write `hh2' "\begin{c -(}table{c )-}[h!]\footnotesize" _newline
{txt}
{com}. file write `hh2' "  \centering" _newline
{txt}
{com}. file write `hh2' "  \caption{c -(}Summary Statistics by Attrition Status{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:sumstat_attrition{c )-}" _newline
{txt}
{com}. file write `hh2' "\scalebox{c -(}1{c )-}{c -(}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}threeparttable{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "\begin{c -(}tabular{c )-}{c -(}lccccc{c )-}\toprule" _newline
{txt}
{com}. 
.   
. file write `hh2' " Dependent Variable & Tracked & Attrition & Difference & N   \\\midrule\midrule" _newline
{txt}
{com}. file write `hh2' " DT score^{c -(}a{c )-} & " %04.3f (mean_tr_bl[1,1]) " & " %04.3f (mean_ct_bl[1,1]) " & " %04.3f (r2_DT_score_pre_std_mean[1,1]) `star_DT_score_pre_std' " & " (n_bl[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_bl[1,1]) " ] & [ " %04.3f (sd_ct_bl[1,1]) " ] & ( " %04.3f (r2_DT_score_pre_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (rwolf_p1) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. 
. file write `hh2' " RSES^{c -(}a{c )-} & " %04.3f (mean_tr_bl[1,2]) " & " %04.3f (mean_ct_bl[1,2]) " & " %04.3f (r2_rosen_pre_std_mean[1,1]) `star_rosen_pre_std' " & "  (n_bl[1,2]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_bl[1,2]) " ] & [ " %04.3f (sd_ct_bl[1,2]) " ] & ( " %04.3f (r2_rosen_pre_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (rwolf_p2) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " CPCS^{c -(}a{c )-} & " %04.3f (mean_tr_bl[1,3]) " & " %04.3f (mean_ct_bl[1,3]) " & " %04.3f (r2_cpcs_pre_std_mean[1,1]) `star_cpcs_pre_std' " & "  (n_bl[1,3]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_bl[1,3]) " ] & [ " %04.3f (sd_ct_bl[1,3]) " ] & ( " %04.3f (r2_cpcs_pre_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (rwolf_p3) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Female & " %04.3f (mean_tr_bl[1,4]) " & " %04.3f (mean_ct_bl[1,4]) " & " %04.3f (r2_female_mean[1,1]) `star_female' " & "  (n_bl[1,4]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_bl[1,4]) " ] & [ " %04.3f (sd_ct_bl[1,4]) " ] & ( " %04.3f (r2_female_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (rwolf_p4) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Grade 3 & " %04.3f (mean_tr_bl[1,5]) " & " %04.3f (mean_ct_bl[1,5]) " & " %04.3f (r2_grade_2_mean[1,1]) `star_grade_2' " & "  (n_bl[1,5]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_bl[1,5]) " ] & [ " %04.3f (sd_ct_bl[1,5]) " ] & ( " %04.3f (r2_grade_2_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (rwolf_p5) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " \\ "_newline
{txt}
{com}. 
. 
. 
. file write `hh2' "\midrule" _newline
{txt}
{com}. file write `hh2' "\end{c -(}tabular{c )-}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\item (a) Variables are standardized using the average and variance of the baseline sample in the March 2016 survey. " _newline
{txt}
{com}. file write `hh2' "\item (b) Standard deviations are reported in square brackets." _newline
{txt}
{com}. file write `hh2' "\item (c) Wild clustered bootstrap p-values are reported within parentheses. Clusters are schools at the baseline. There are 34 clusters. " _newline
{txt}
{com}. file write `hh2' "\item (d) Romano-Wolf multiple hypothesis testing p-values are reported in curly brackets." _newline
{txt}
{com}. file write `hh2' "\item (e) Statistical significance is indicated by stars based on the wild clustered bootstrap p-values reported in parentheses: $*$ denotes significance at the 10\% level, $∗∗$ at the 5\% level, and $∗∗∗$ at the 1\% level. " _newline
{txt}
{com}. file write `hh2' "\end{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}threeparttable{c )-}" _newline
{txt}
{com}. file write `hh2' "{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:addlabel{c )-}%" _newline
{txt}
{com}. file write `hh2' "\end{c -(}table{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. file close `hh2'
{txt}
{com}. 
. 
. 
. /// Differences in 2*2 groups
> /// A = Tracked & Treated
> /// B = Tracked & Control
> /// C = Attrition & Treated
> /// D = Attrition & Control
> 
. gen group_A = 1 if attrition == 0 & treatment == 1
{txt}(910 missing values generated)

{com}. gen group_B = 1 if attrition == 0 & treatment == 0
{txt}(957 missing values generated)

{com}. gen group_C = 1 if attrition == 1 & treatment == 1
{txt}(673 missing values generated)

{com}. gen group_D = 1 if attrition == 1 & treatment == 0
{txt}(675 missing values generated)

{com}. recode group_* (.=0)
{txt}(910 changes made to {bf:group_A})
(957 changes made to {bf:group_B})
(673 changes made to {bf:group_C})
(675 changes made to {bf:group_D})

{com}. 
. preserve
{txt}
{com}. 
. /// A vs C
> restore
{txt}
{com}. preserve
{txt}
{com}. keep if group_A == 1 | group_C == 1
{txt}(528 observations deleted)

{com}. local dummy group_A
{txt}
{com}. 
. // Mean difference
. foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat DT_score_pre_std rosen_pre_std cpcs_pre_std female grade_2 if `dummy' == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_track_treat_bl = r(StatTotal)
{txt}  5{com}. 
. tabstat DT_score_pre_std rosen_pre_std cpcs_pre_std female grade_2 if `dummy' == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_attrition_treat_bl = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d    female   grade_2
{hline 9}{c +}{hline 50}
{ralign 8:N} {...}
{c |}{...}
 {res}      144       145       145       145       145
{txt}{hline 9}{c BT}{hline 50}
{res}
{txt}r(StatTotal)[1,5]
   DT_score_p~d  rosen_pre_~d  cpcs_pre_std        female       grade_2
N {res}          144           145           145           145           145
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d    female   grade_2
{hline 9}{c +}{hline 50}
{ralign 8:N} {...}
{c |}{...}
 {res}      374       382       382       382       382
{txt}{hline 9}{c BT}{hline 50}
{res}
{txt}r(StatTotal)[1,5]
   DT_score_p~d  rosen_pre_~d  cpcs_pre_std        female       grade_2
N {res}          374           382           382           382           382
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d    female   grade_2
{hline 9}{c +}{hline 50}
{ralign 8:Mean} {...}
{c |}{...}
 {res}  .131626  .0147665  .1086469  .6068966  .6137931
{txt}{hline 9}{c BT}{hline 50}
{res}
{txt}r(StatTotal)[1,5]
      DT_score_p~d  rosen_pre_~d  cpcs_pre_std        female       grade_2
Mean {res}    .13162601     .01476645     .10864695     .60689655      .6137931
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d    female   grade_2
{hline 9}{c +}{hline 50}
{ralign 8:Mean} {...}
{c |}{...}
 {res}-.0571177 -.0219281  .0621693  .5628272   .591623
{txt}{hline 9}{c BT}{hline 50}
{res}
{txt}r(StatTotal)[1,5]
      DT_score_p~d  rosen_pre_~d  cpcs_pre_std        female       grade_2
Mean {res}   -.05711768    -.02192813      .0621693     .56282723     .59162304
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d    female   grade_2
{hline 9}{c +}{hline 50}
{ralign 8:SD} {...}
{c |}{...}
 {res} .9720702  .9813127  .9714465  .4901325  .4885666
{txt}{hline 9}{c BT}{hline 50}
{res}
{txt}r(StatTotal)[1,5]
    DT_score_p~d  rosen_pre_~d  cpcs_pre_std        female       grade_2
SD {res}    .97207023     .98131273     .97144648     .49013252      .4885666
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d    female   grade_2
{hline 9}{c +}{hline 50}
{ralign 8:SD} {...}
{c |}{...}
 {res} 1.010309  .9292978  .9123191  .4966876  .4921782
{txt}{hline 9}{c BT}{hline 50}
{res}
{txt}r(StatTotal)[1,5]
    DT_score_p~d  rosen_pre_~d  cpcs_pre_std        female       grade_2
SD {res}     1.010309     .92929779     .91231914     .49668758     .49217817
{reset}
{com}. 
. 
. 
. foreach dep in DT_score_pre_std rosen_pre_std cpcs_pre_std female grade_2{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' `dummy', cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_treat_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_treat_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_treat_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_treat_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:group_A = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:group_A}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 53}{txt}Number of obs{col 71} = {res} 518
{txt}Linear regression{col 53}{txt}Number of clusters{col 71} = {res}  17
{col 53}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 68}{txt}min{col 71} = {res}  25
{txt}Error weight: Rademacher{col 68}{txt}avg{col 71} = {res}30.5
{col 68}{txt}max{col 71} = {res}  38
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        DT_score_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}             group_A = 0{col 26}{c |}{result}{space 2} .1887437{col 38}{space 1}   1.98{col 46}{space 3}0.066{col 54}{space 3}-.0099351{col 66}{space 3} .3915403
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:group_A = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:group_A}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 53}{txt}Number of obs{col 71} = {res} 527
{txt}Linear regression{col 53}{txt}Number of clusters{col 71} = {res}  17
{col 53}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 68}{txt}min{col 71} = {res}  26
{txt}Error weight: Rademacher{col 68}{txt}avg{col 71} = {res}31.0
{col 68}{txt}max{col 71} = {res}  38
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}           rosen_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}             group_A = 0{col 26}{c |}{result}{space 2} .0366946{col 38}{space 1}   0.27{col 46}{space 3}0.794{col 54}{space 3}-.2346314{col 66}{space 3} .3311443
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:group_A = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:group_A}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 53}{txt}Number of obs{col 71} = {res} 527
{txt}Linear regression{col 53}{txt}Number of clusters{col 71} = {res}  17
{col 53}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 68}{txt}min{col 71} = {res}  26
{txt}Error weight: Rademacher{col 68}{txt}avg{col 71} = {res}31.0
{col 68}{txt}max{col 71} = {res}  38
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}            cpcs_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}             group_A = 0{col 26}{c |}{result}{space 2} .0464776{col 38}{space 1}   0.38{col 46}{space 3}0.746{col 54}{space 3} -.211049{col 66}{space 3} .2858773
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:group_A = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:group_A}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 53}{txt}Number of obs{col 71} = {res} 527
{txt}Linear regression{col 53}{txt}Number of clusters{col 71} = {res}  17
{col 53}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 68}{txt}min{col 71} = {res}  26
{txt}Error weight: Rademacher{col 68}{txt}avg{col 71} = {res}31.0
{col 68}{txt}max{col 71} = {res}  38
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                  female{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}             group_A = 0{col 26}{c |}{result}{space 2} .0440693{col 38}{space 1}   0.96{col 46}{space 3}0.316{col 54}{space 3}-.0526316{col 66}{space 3} .1542035
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:group_A = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:group_A}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 53}{txt}Number of obs{col 71} = {res} 527
{txt}Linear regression{col 53}{txt}Number of clusters{col 71} = {res}  17
{col 53}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 68}{txt}min{col 71} = {res}  26
{txt}Error weight: Rademacher{col 68}{txt}avg{col 71} = {res}31.0
{col 68}{txt}max{col 71} = {res}  38
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                 grade_2{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}             group_A = 0{col 26}{c |}{result}{space 2} .0221701{col 38}{space 1}   0.36{col 46}{space 3}0.732{col 54}{space 3}-.1117326{col 66}{space 3} .1605764
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. 
. // significant level
. 
. local outcome DT_score_pre_std rosen_pre_std cpcs_pre_std female grade_2
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}.                 if r2_`dep'_treat_pv[1,1]<=0.01 {c -(}
{txt}  3{com}.                         local star_`dep'_treat %3s "***"
{txt}  4{com}.                 {c )-}
{txt}  5{com}.                 else if (r2_`dep'_treat_pv[1,1]>0.01) & (r2_`dep'_treat_pv[1,1]<=0.05) {c -(}
{txt}  6{com}.                         local star_`dep'_treat %2s "**"
{txt}  7{com}.                 {c )-}
{txt}  8{com}.                 else if (r2_`dep'_treat_pv[1,1]>0.05) & (r2_`dep'_treat_pv[1,1]<=0.10) {c -(}
{txt}  9{com}.                         local star_`dep'_treat %1s "*"
{txt} 10{com}.                 {c )-}
{txt} 11{com}.                 else {c -(}
{txt} 12{com}.                         local star_`dep'_treat  ""
{txt} 13{com}.                 {c )-}
{txt} 14{com}. {c )-} 
{txt}
{com}. 
. rwolf DT_score_pre_std rosen_pre_std cpcs_pre_std female grade_2, indepvar(`dummy') reps(1000) cluster(school_no) vce(cluster school_no)
Bootstrap replications (1000). This may take some time.
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Romano-Wolf step-down adjusted p-values


Independent variable:  group_A
Outcome variables:   DT_score_pre_std rosen_pre_std cpcs_pre_std female grade_2
Number of resamples: 1000


{hline 78}
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
{hline 20}+{hline 57}
   {txt}DT_score_pre_std {c |}    {res}0.0648             0.0649              0.2458
      {txt}rosen_pre_std {c |}    {res}0.7882             0.8072              0.9471
       {txt}cpcs_pre_std {c |}    {res}0.7072             0.7113              0.9471
             {txt}female {c |}    {res}0.3536             0.3516              0.7852
            {txt}grade_2 {c |}    {res}0.7252             0.7522              0.9471
{hline 78}
{txt}
{com}. scalar rwolf_p1_treat = e(rw_DT_score_pre_std)
{txt}
{com}. scalar rwolf_p2_treat = e(rw_rosen_pre_std)
{txt}
{com}. scalar rwolf_p3_treat = e(rw_cpcs_pre_std)
{txt}
{com}. scalar rwolf_p4_treat = e(rw_female)
{txt}
{com}. scalar rwolf_p5_treat = e(rw_grade_2)
{txt}
{com}. 
. 
. 
. /// B vs D
> restore
{txt}
{com}. preserve
{txt}
{com}. keep if group_B == 1 | group_D == 1
{txt}(577 observations deleted)

{com}. local dummy group_B
{txt}
{com}. 
. // Mean difference
. foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat DT_score_pre_std rosen_pre_std cpcs_pre_std female grade_2 if `dummy' == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_track_control_bl = r(StatTotal)
{txt}  5{com}. 
. tabstat DT_score_pre_std rosen_pre_std cpcs_pre_std female grade_2 if `dummy' == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_attrition_control_bl = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d    female   grade_2
{hline 9}{c +}{hline 50}
{ralign 8:N} {...}
{c |}{...}
 {res}       95        98        98        98        98
{txt}{hline 9}{c BT}{hline 50}
{res}
{txt}r(StatTotal)[1,5]
   DT_score_p~d  rosen_pre_~d  cpcs_pre_std        female       grade_2
N {res}           95            98            98            98            98
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d    female   grade_2
{hline 9}{c +}{hline 50}
{ralign 8:N} {...}
{c |}{...}
 {res}      355       380       380       380       380
{txt}{hline 9}{c BT}{hline 50}
{res}
{txt}r(StatTotal)[1,5]
   DT_score_p~d  rosen_pre_~d  cpcs_pre_std        female       grade_2
N {res}          355           380           380           380           380
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d    female   grade_2
{hline 9}{c +}{hline 50}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .2065588 -.0808116  -.240825  .6122449  .6632653
{txt}{hline 9}{c BT}{hline 50}
{res}
{txt}r(StatTotal)[1,5]
      DT_score_p~d  rosen_pre_~d  cpcs_pre_std        female       grade_2
Mean {res}    .20655877    -.08081157    -.24082502      .6122449     .66326531
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d    female   grade_2
{hline 9}{c +}{hline 50}
{ralign 8:Mean} {...}
{c |}{...}
 {res}-.0484936    .03725 -.0418459  .5894737  .5342105
{txt}{hline 9}{c BT}{hline 50}
{res}
{txt}r(StatTotal)[1,5]
      DT_score_p~d  rosen_pre_~d  cpcs_pre_std        female       grade_2
Mean {res}   -.04849356     .03725002    -.04184593     .58947368     .53421053
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d    female   grade_2
{hline 9}{c +}{hline 50}
{ralign 8:SD} {...}
{c |}{...}
 {res} .9189083  1.045447  1.124362  .4897433  .4750231
{txt}{hline 9}{c BT}{hline 50}
{res}
{txt}r(StatTotal)[1,5]
    DT_score_p~d  rosen_pre_~d  cpcs_pre_std        female       grade_2
SD {res}    .91890826     1.0454466     1.1243618     .48974332     .47502312
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d    female   grade_2
{hline 9}{c +}{hline 50}
{ralign 8:SD} {...}
{c |}{...}
 {res} 1.012869  1.071214  1.058732  .4925779  .4994859
{txt}{hline 9}{c BT}{hline 50}
{res}
{txt}r(StatTotal)[1,5]
    DT_score_p~d  rosen_pre_~d  cpcs_pre_std        female       grade_2
SD {res}    1.0128693     1.0712145      1.058732     .49257788     .49948592
{reset}
{com}. 
. foreach dep in DT_score_pre_std rosen_pre_std cpcs_pre_std female grade_2{c -(}
{txt}  2{com}.     wildbootstrap reg `dep' `dummy', cluster(school_no) reps(1000)
{txt}  3{com}.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_control_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_control_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_control_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_control_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:group_B = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:group_B}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 53}{txt}Number of obs{col 71} = {res} 450
{txt}Linear regression{col 53}{txt}Number of clusters{col 71} = {res}  17
{col 53}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 68}{txt}min{col 71} = {res}  15
{txt}Error weight: Rademacher{col 68}{txt}avg{col 71} = {res}26.5
{col 68}{txt}max{col 71} = {res}  30
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        DT_score_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}             group_B = 0{col 26}{c |}{result}{space 2} .2550523{col 38}{space 1}   1.80{col 46}{space 3}0.086{col 54}{space 3}-.0266011{col 66}{space 3} .5954419
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:group_B = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:group_B}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{res}
{txt}Wild cluster bootstrap{col 53}{txt}Number of obs{col 71} = {res} 478
{txt}Linear regression{col 53}{txt}Number of clusters{col 71} = {res}  17
{col 53}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 68}{txt}min{col 71} = {res}  16
{txt}Error weight: Rademacher{col 68}{txt}avg{col 71} = {res}28.1
{col 68}{txt}max{col 71} = {res}  30
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}           rosen_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}             group_B = 0{col 26}{c |}{result}{space 2}-.1180616{col 38}{space 1}  -1.14{col 46}{space 3}0.278{col 54}{space 3}-.3429616{col 66}{space 3} .1193305
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:group_B = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:group_B}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 53}{txt}Number of obs{col 71} = {res} 478
{txt}Linear regression{col 53}{txt}Number of clusters{col 71} = {res}  17
{col 53}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 68}{txt}min{col 71} = {res}  16
{txt}Error weight: Rademacher{col 68}{txt}avg{col 71} = {res}28.1
{col 68}{txt}max{col 71} = {res}  30
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}            cpcs_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}             group_B = 0{col 26}{c |}{result}{space 2}-.1989791{col 38}{space 1}  -2.31{col 46}{space 3}0.068{col 54}{space 3}-.3852102{col 66}{space 3} .0141801
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:group_B = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:group_B}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 53}{txt}Number of obs{col 71} = {res} 478
{txt}Linear regression{col 53}{txt}Number of clusters{col 71} = {res}  17
{col 53}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 68}{txt}min{col 71} = {res}  16
{txt}Error weight: Rademacher{col 68}{txt}avg{col 71} = {res}28.1
{col 68}{txt}max{col 71} = {res}  30
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                  female{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}             group_B = 0{col 26}{c |}{result}{space 2} .0227712{col 38}{space 1}   0.51{col 46}{space 3}0.624{col 54}{space 3}-.0905768{col 66}{space 3} .1280886
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:group_B = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:group_B}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 53}{txt}Number of obs{col 71} = {res} 478
{txt}Linear regression{col 53}{txt}Number of clusters{col 71} = {res}  17
{col 53}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 68}{txt}min{col 71} = {res}  16
{txt}Error weight: Rademacher{col 68}{txt}avg{col 71} = {res}28.1
{col 68}{txt}max{col 71} = {res}  30
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                 grade_2{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}             group_B = 0{col 26}{c |}{result}{space 2} .1290548{col 38}{space 1}   1.51{col 46}{space 3}0.146{col 54}{space 3}-.0516255{col 66}{space 3} .3076223
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. // significant level
. 
. local outcome DT_score_pre_std rosen_pre_std cpcs_pre_std female grade_2
{txt}
{com}. foreach dep in `outcome'{c -(}
{txt}  2{com}.                 if r2_`dep'_control_pv[1,1]<=0.01 {c -(}
{txt}  3{com}.                         local star_`dep'_control %3s "***"
{txt}  4{com}.                 {c )-}
{txt}  5{com}.                 else if (r2_`dep'_control_pv[1,1]>0.01) & (r2_`dep'_control_pv[1,1]<=0.05) {c -(}
{txt}  6{com}.                         local star_`dep'_control %2s "**"
{txt}  7{com}.                 {c )-}
{txt}  8{com}.                 else if (r2_`dep'_control_pv[1,1]>0.05) & (r2_`dep'_control_pv[1,1]<=0.10) {c -(}
{txt}  9{com}.                         local star_`dep'_control %1s "*"
{txt} 10{com}.                 {c )-}
{txt} 11{com}.                 else {c -(}
{txt} 12{com}.                         local star_`dep'_control  ""
{txt} 13{com}.                 {c )-}
{txt} 14{com}. {c )-} 
{txt}
{com}. 
. rwolf DT_score_pre_std rosen_pre_std cpcs_pre_std female grade_2, indepvar(`dummy') reps(1000) cluster(school_no) vce(cluster school_no)
Bootstrap replications (1000). This may take some time.
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Romano-Wolf step-down adjusted p-values


Independent variable:  group_B
Outcome variables:   DT_score_pre_std rosen_pre_std cpcs_pre_std female grade_2
Number of resamples: 1000


{hline 78}
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
{hline 20}+{hline 57}
   {txt}DT_score_pre_std {c |}    {res}0.0904             0.0999              0.3277
      {txt}rosen_pre_std {c |}    {res}0.2711             0.2717              0.4745
       {txt}cpcs_pre_std {c |}    {res}0.0346             0.0380              0.1758
             {txt}female {c |}    {res}0.6158             0.6094              0.6094
            {txt}grade_2 {c |}    {res}0.1501             0.1698              0.4206
{hline 78}
{txt}
{com}. scalar rwolf_p1_control = e(rw_DT_score_pre_std)
{txt}
{com}. scalar rwolf_p2_control = e(rw_rosen_pre_std)
{txt}
{com}. scalar rwolf_p3_control = e(rw_cpcs_pre_std)
{txt}
{com}. scalar rwolf_p4_control = e(rw_female)
{txt}
{com}. scalar rwolf_p5_control = e(rw_grade_2)
{txt}
{com}. 
. 
. /// Table
> tempname hh2
{txt}
{com}. file open `hh2' using "$path_output/summary_stat_baseline_attrition_treatment.tex", write replace
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "% Author: Kazuma Takakura" _newline
{txt}
{com}. file write `hh2' "% Date: `c(current_date)'" _newline
{txt}
{com}. file write `hh2' "% Time: `c(current_time)'" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. 
. file write `hh2' "\begin{c -(}table{c )-}[h!]\footnotesize" _newline
{txt}
{com}. file write `hh2' "  \centering" _newline
{txt}
{com}. file write `hh2' "  \caption{c -(}Summary Statistics by Attrition Status and Treatment Status{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:difference_attrition_treatment{c )-}" _newline
{txt}
{com}. file write `hh2' "\scalebox{c -(}0.9{c )-}{c -(}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}threeparttable{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "\begin{c -(}tabular{c )-}{c -(}lccccccc{c )-}\toprule" _newline
{txt}
{com}. 
.   
. file write `hh2' " Dependent Variable & Tracked-Treated & Attrition-Treated  & Difference  & Tracked-Control & Attrition-Control & Difference   \\\midrule\midrule" _newline
{txt}
{com}. file write `hh2' " DT score^{c -(}a{c )-} & " %04.3f (mean_track_treat_bl[1,1]) " & " %04.3f (mean_attrition_treat_bl[1,1]) " & " %04.3f (r2_DT_score_pre_std_treat_mean[1,1]) `star_DT_score_pre_std_treat' " & " %04.3f (mean_track_control_bl[1,1]) " & " %04.3f (mean_attrition_control_bl[1,1]) " & " %04.3f (r2_DT_score_pre_std_control_mean[1,1]) `star_DT_score_pre_std_control' "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_track_treat_bl[1,1]) " ] & [ " %04.3f (sd_attrition_treat_bl[1,1]) " ] & ( " %04.3f (r2_DT_score_pre_std_treat_pv[1,1]) " )  & [ " %04.3f (sd_track_control_bl[1,1]) " ] & [ " %04.3f (sd_attrition_control_bl[1,1]) " ] & ( " %04.3f (r2_DT_score_pre_std_control_pv[1,1]) " ) \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (rwolf_p1_treat) " \{c )-} &  &   & \{c -(} " %04.3f (rwolf_p1_control) " \{c )-}  \\ " _newline
{txt}
{com}. 
. 
. file write `hh2' " RSES^{c -(}a{c )-} & " %04.3f (mean_track_treat_bl[1,2]) " & " %04.3f (mean_attrition_treat_bl[1,2]) " & " %04.3f (r2_rosen_pre_std_treat_mean[1,1]) `star_rosen_pre_std_treat' " & " %04.3f (mean_track_control_bl[1,2]) " & " %04.3f (mean_attrition_control_bl[1,2]) " & " %04.3f (r2_DT_score_pre_std_control_mean[1,1]) `star_DT_score_pre_std_control' " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_track_treat_bl[1,2]) " ] & [ " %04.3f (sd_attrition_treat_bl[1,2]) " ] & ( " %04.3f (r2_rosen_pre_std_treat_pv[1,1]) " )   & [ " %04.3f (sd_track_control_bl[1,2]) " ] & [ " %04.3f (sd_attrition_control_bl[1,2]) " ] & ( " %04.3f (r2_rosen_pre_std_control_pv[1,1]) " )  \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (rwolf_p2_treat) " \{c )-} &  &   & \{c -(} " %04.3f (rwolf_p2_control) " \{c )-}  \\ " _newline
{txt}
{com}. 
. file write `hh2' " CPCS^{c -(}a{c )-} & " %04.3f (mean_track_treat_bl[1,3]) " & " %04.3f (mean_attrition_treat_bl[1,3]) " & " %04.3f (r2_cpcs_pre_std_treat_mean[1,1]) `star_cpcs_pre_std_treat' " & " %04.3f (mean_track_control_bl[1,3]) " & " %04.3f (mean_attrition_control_bl[1,3]) " & " %04.3f (r2_cpcs_pre_std_control_mean[1,1]) `star_cpcs_pre_std_control' " \\ " _newline
{txt}
{com}. file write `hh2' "       & [ " %04.3f (sd_track_treat_bl[1,3]) " ] & [ " %04.3f (sd_attrition_treat_bl[1,3]) " ] & ( " %04.3f (r2_cpcs_pre_std_treat_pv[1,1]) " )  & [ " %04.3f (sd_track_control_bl[1,3]) " ] & [ " %04.3f (sd_attrition_control_bl[1,3]) " ] & ( " %04.3f (r2_cpcs_pre_std_control_pv[1,1]) " )  \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (rwolf_p3_treat) " \{c )-} &   &   & \{c -(} " %04.3f (rwolf_p3_control) " \{c )-} \\ " _newline
{txt}
{com}. 
. file write `hh2' " Female & " %04.3f (mean_track_treat_bl[1,4]) " & " %04.3f (mean_attrition_treat_bl[1,4]) " & " %04.3f (r2_female_treat_mean[1,1]) `star_female_treat' " & " %04.3f (mean_track_control_bl[1,4]) " & " %04.3f (mean_attrition_control_bl[1,4]) " & " %04.3f (r2_female_control_mean[1,1]) `star_female_control' " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_track_treat_bl[1,4]) " ] & [ " %04.3f (sd_attrition_treat_bl[1,4]) " ] & ( " %04.3f (r2_female_treat_pv[1,1]) " )   & [ " %04.3f (sd_track_control_bl[1,4]) " ] & [ " %04.3f (sd_attrition_control_bl[1,4]) " ] & ( " %04.3f (r2_female_control_pv[1,1]) " )   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (rwolf_p4_treat) " \{c )-} &  &   & \{c -(} " %04.3f (rwolf_p4_control) " \{c )-}  \\ " _newline
{txt}
{com}. 
. file write `hh2' " Grade 3 & " %04.3f (mean_track_treat_bl[1,5]) " & " %04.3f (mean_attrition_treat_bl[1,5]) " & " %04.3f (r2_grade_2_treat_mean[1,1]) `star_grade_2_treat' " & " %04.3f (mean_track_control_bl[1,5]) " & " %04.3f (mean_attrition_control_bl[1,5]) " & " %04.3f (r2_grade_2_control_mean[1,1]) `star_grade_2_control' " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_track_treat_bl[1,5]) " ] & [ " %04.3f (sd_attrition_treat_bl[1,5]) " ] & ( " %04.3f (r2_grade_2_treat_pv[1,1]) " )   & [ " %04.3f (sd_track_control_bl[1,5]) " ] & [ " %04.3f (sd_attrition_control_bl[1,5]) " ] & ( " %04.3f (r2_grade_2_control_pv[1,1]) " )   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (rwolf_p5_treat) " \{c )-} &   &   & \{c -(} " %04.3f (rwolf_p5_control) " \{c )-}  \\ " _newline
{txt}
{com}. 
. file write `hh2' " \\ "_newline
{txt}
{com}. 
. 
. file write `hh2' "\midrule" _newline
{txt}
{com}. file write `hh2' "\end{c -(}tabular{c )-}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\item (a) Variables are standardized using the average and variance of the whole baseline sample in the March 2016 survey. " _newline
{txt}
{com}. file write `hh2' "\item (b) Standard deviations are reported in square brackets." _newline
{txt}
{com}. file write `hh2' "\item (c) Wild clustered bootstrap p-values are reported within parentheses. Clusters are schools at the baseline. There are 34 clusters. " _newline
{txt}
{com}. file write `hh2' "\item (d) Romano-Wolf multiple hypothesis testing p-values are reported in curly brackets." _newline
{txt}
{com}. file write `hh2' "\item (e) $^*$ Significant at 10\% level; $^{c -(}**{c )-}$ significant at 5\% level; $^{c -(}***{c )-}$ significant at 1\% level. " _newline
{txt}
{com}. file write `hh2' "\end{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}threeparttable{c )-}" _newline
{txt}
{com}. file write `hh2' "{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:addlabel{c )-}%" _newline
{txt}
{com}. file write `hh2' "\end{c -(}table{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. file close `hh2'
{txt}
{com}. 
. 
{txt}end of do-file

{com}. 
. do "$path_do/3_table_F1.do"
{txt}
{com}. * This is the do file to create "Table F1. Difference in Differences"
. set seed 123
{txt}
{com}. 
. use "$path_data/temp/followup_student_parents_matched", clear
{txt}
{com}. 
. gen gend = q1d - 1
{txt}
{com}. 
. local controls i.grade gend branch1 branch2 branch3 income_source1 income_source2 income_source3 income_source4 last_income_per_member hhmember hhheadage hhheadeduyear age_tchr phone_survey
{txt}
{com}. 
. 
. // Cog
. preserve
{txt}
{com}. rename followup_cog_std outcome_1
{res}{txt}
{com}. rename DT_score_pre_std_missing_0 outcome_0
{res}{txt}
{com}. reshape long outcome_, i(student_no) j(time)
{txt}(j = 0 1)

Data{col 36}Wide{col 43}->{col 48}Long
{hline 77}
Number of observations     {res}         243   {txt}->   {res}486         
{txt}Number of variables        {res}       1,260   {txt}->   {res}1,260       
{txt}j variable (2 values)                     ->   {res}time
{txt}xij variables:
                    {res}outcome_0 outcome_1   {txt}->   {res}outcome_
{txt}{hline 77}

{com}. reg outcome_ i.treatment##i.time DT_score_pre_std_missing_dummy cpcs_pre_std_missing_0 rosen_pre_std_missing_0 `controls', vce(boot, cluster(school_no) reps(1000))
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{text}Bootstrap replications ({result:1,000}){text}: 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done
{res}{text}{text:{bf:x}}: Error occurred when {bf:bootstrap} executed {bf:regress}.
{res}
{txt}{col 1}Linear regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:486}
{txt}{col 57}{lalign 13:Replications}{col 70} = {res}{ralign 6:972}
{txt}{col 57}{lalign 13:Wald chi2({res:21})}{col 70} = {res}{ralign 6:41.14}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0054}
{txt}{col 57}{lalign 13:R-squared}{col 70} = {res}{ralign 6:0.1029}
{txt}{col 57}{lalign 13:Adj R-squared}{col 70} = {res}{ralign 6:0.0623}
{txt}{col 57}{lalign 13:Root MSE}{col 70} = {res}{ralign 6:0.9633}

{txt}{ralign 96:(Replications based on {res:33} clusters in {res:school_no})}
{hline 31}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 32}{c |}   Observed{col 44}   Bootstrap{col 72}         Norm{col 85}al-based
{col 1}                      outcome_{col 32}{c |} coefficient{col 44}  std. err.{col 56}      z{col 64}   P>|z|{col 72}     [95% con{col 85}f. interval]
{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}1.treatment {c |}{col 32}{res}{space 2}-.2335824{col 44}{space 2} .2182351{col 55}{space 1}   -1.07{col 64}{space 3}0.284{col 72}{space 4}-.6613153{col 85}{space 3} .1941505
{txt}{space 24}1.time {c |}{col 32}{res}{space 2} .0901164{col 44}{space 2} .1774831{col 55}{space 1}    0.51{col 64}{space 3}0.612{col 72}{space 4} -.257744{col 85}{space 3} .4379768
{txt}{space 30} {c |}
{space 16}treatment#time {c |}
{space 26}1 1  {c |}{col 32}{res}{space 2}-.1510225{col 44}{space 2} .2324458{col 55}{space 1}   -0.65{col 64}{space 3}0.516{col 72}{space 4} -.606608{col 85}{space 3}  .304563
{txt}{space 30} {c |}
DT_score_pre_std_missing_dummy {c |}{col 32}{res}{space 2}-.4866693{col 44}{space 2} .3989816{col 55}{space 1}   -1.22{col 64}{space 3}0.223{col 72}{space 4}-1.268659{col 85}{space 3} .2953203
{txt}{space 8}cpcs_pre_std_missing_0 {c |}{col 32}{res}{space 2} .4097287{col 44}{space 2} .1198958{col 55}{space 1}    3.42{col 64}{space 3}0.001{col 72}{space 4} .1747372{col 85}{space 3} .6447202
{txt}{space 7}rosen_pre_std_missing_0 {c |}{col 32}{res}{space 2}-.3668043{col 44}{space 2} .1232457{col 55}{space 1}   -2.98{col 64}{space 3}0.003{col 72}{space 4}-.6083614{col 85}{space 3}-.1252471
{txt}{space 23}4.grade {c |}{col 32}{res}{space 2} -.126914{col 44}{space 2} .1494247{col 55}{space 1}   -0.85{col 64}{space 3}0.396{col 72}{space 4} -.419781{col 85}{space 3}  .165953
{txt}{space 26}gend {c |}{col 32}{res}{space 2}-.2261973{col 44}{space 2} .0953114{col 55}{space 1}   -2.37{col 64}{space 3}0.018{col 72}{space 4}-.4130042{col 85}{space 3}-.0393904
{txt}{space 23}branch1 {c |}{col 32}{res}{space 2}-.1194302{col 44}{space 2} .2224473{col 55}{space 1}   -0.54{col 64}{space 3}0.591{col 72}{space 4}-.5554188{col 85}{space 3} .3165584
{txt}{space 23}branch2 {c |}{col 32}{res}{space 2} .0958577{col 44}{space 2} .2632865{col 55}{space 1}    0.36{col 64}{space 3}0.716{col 72}{space 4}-.4201745{col 85}{space 3} .6118898
{txt}{space 23}branch3 {c |}{col 32}{res}{space 2}-.2515242{col 44}{space 2}  .205098{col 55}{space 1}   -1.23{col 64}{space 3}0.220{col 72}{space 4}-.6535088{col 85}{space 3} .1504604
{txt}{space 16}income_source1 {c |}{col 32}{res}{space 2}-.1841412{col 44}{space 2} .3151367{col 55}{space 1}   -0.58{col 64}{space 3}0.559{col 72}{space 4}-.8017977{col 85}{space 3} .4335154
{txt}{space 16}income_source2 {c |}{col 32}{res}{space 2}-.1488446{col 44}{space 2} .2274564{col 55}{space 1}   -0.65{col 64}{space 3}0.513{col 72}{space 4}-.5946511{col 85}{space 3} .2969618
{txt}{space 16}income_source3 {c |}{col 32}{res}{space 2}-.2142481{col 44}{space 2} .2196989{col 55}{space 1}   -0.98{col 64}{space 3}0.329{col 72}{space 4}  -.64485{col 85}{space 3} .2163538
{txt}{space 16}income_source4 {c |}{col 32}{res}{space 2} .5451894{col 44}{space 2} .7055148{col 55}{space 1}    0.77{col 64}{space 3}0.440{col 72}{space 4}-.8375941{col 85}{space 3} 1.927973
{txt}{space 8}last_income_per_member {c |}{col 32}{res}{space 2} .0000281{col 44}{space 2} .0000451{col 55}{space 1}    0.62{col 64}{space 3}0.533{col 72}{space 4}-.0000603{col 85}{space 3} .0001166
{txt}{space 22}hhmember {c |}{col 32}{res}{space 2}-.0428586{col 44}{space 2} .0348665{col 55}{space 1}   -1.23{col 64}{space 3}0.219{col 72}{space 4}-.1111958{col 85}{space 3} .0254786
{txt}{space 21}hhheadage {c |}{col 32}{res}{space 2} -.005095{col 44}{space 2} .0064134{col 55}{space 1}   -0.79{col 64}{space 3}0.427{col 72}{space 4}-.0176651{col 85}{space 3}  .007475
{txt}{space 17}hhheadeduyear {c |}{col 32}{res}{space 2} .0058549{col 44}{space 2} .0141908{col 55}{space 1}    0.41{col 64}{space 3}0.680{col 72}{space 4}-.0219586{col 85}{space 3} .0336685
{txt}{space 22}age_tchr {c |}{col 32}{res}{space 2}-.0059464{col 44}{space 2} .0120892{col 55}{space 1}   -0.49{col 64}{space 3}0.623{col 72}{space 4}-.0296408{col 85}{space 3}  .017748
{txt}{space 18}phone_survey {c |}{col 32}{res}{space 2} .2349326{col 44}{space 2} .1152512{col 55}{space 1}    2.04{col 64}{space 3}0.042{col 72}{space 4} .0090443{col 85}{space 3} .4608208
{txt}{space 25}_cons {c |}{col 32}{res}{space 2} 1.111036{col 44}{space 2} .5890316{col 55}{space 1}    1.89{col 64}{space 3}0.059{col 72}{space 4}-.0434445{col 85}{space 3} 2.265517
{txt}{hline 31}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 79}Note: One or more parameters could not be estimated in 28 bootstrap replicates; standard-error estimates include only complete replications.{p_end}

{com}. matrix did_cog = r(table)
{txt}
{com}. scalar did_cog_n = e(N) / 2
{txt}
{com}. restore
{txt}
{com}. 
. // CPCS
. preserve
{txt}
{com}. keep if CPCS_std != .
{txt}(7 observations deleted)

{com}. rename CPCS_std outcome_1
{res}{txt}
{com}. rename cpcs_pre_std_missing_0 outcome_0
{res}{txt}
{com}. reshape long outcome_, i(student_no) j(time)
{txt}(j = 0 1)

Data{col 36}Wide{col 43}->{col 48}Long
{hline 77}
Number of observations     {res}         236   {txt}->   {res}472         
{txt}Number of variables        {res}       1,260   {txt}->   {res}1,260       
{txt}j variable (2 values)                     ->   {res}time
{txt}xij variables:
                    {res}outcome_0 outcome_1   {txt}->   {res}outcome_
{txt}{hline 77}

{com}. reg outcome_ i.treatment##i.time DT_score_pre_std_missing_0 rosen_pre_std_missing_0 `controls', vce(boot, cluster(school_no) reps(1000))
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{text}Bootstrap replications ({result:1,000}){text}: 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done
{res}{text}{text:{bf:x}}: Error occurred when {bf:bootstrap} executed {bf:regress}.
{res}
{txt}{col 1}Linear regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:472}
{txt}{col 57}{lalign 13:Replications}{col 70} = {res}{ralign 6:977}
{txt}{col 57}{lalign 13:Wald chi2({res:20})}{col 70} = {res}{ralign 6:461.91}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 57}{lalign 13:R-squared}{col 70} = {res}{ralign 6:0.2773}
{txt}{col 57}{lalign 13:Adj R-squared}{col 70} = {res}{ralign 6:0.2453}
{txt}{col 57}{lalign 13:Root MSE}{col 70} = {res}{ralign 6:0.8694}

{txt}{ralign 92:(Replications based on {res:33} clusters in {res:school_no})}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}   Observed{col 40}   Bootstrap{col 68}         Norm{col 81}al-based
{col 1}                  outcome_{col 28}{c |} coefficient{col 40}  std. err.{col 52}      z{col 60}   P>|z|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}1.treatment {c |}{col 28}{res}{space 2} .3351391{col 40}{space 2} .1337089{col 51}{space 1}    2.51{col 60}{space 3}0.012{col 68}{space 4} .0730745{col 81}{space 3} .5972037
{txt}{space 20}1.time {c |}{col 28}{res}{space 2}-.0579986{col 40}{space 2} .1541741{col 51}{space 1}   -0.38{col 60}{space 3}0.707{col 68}{space 4}-.3601742{col 81}{space 3} .2441771
{txt}{space 26} {c |}
{space 12}treatment#time {c |}
{space 22}1 1  {c |}{col 28}{res}{space 2} .0895153{col 40}{space 2} .2663987{col 51}{space 1}    0.34{col 60}{space 3}0.737{col 68}{space 4}-.4326166{col 81}{space 3} .6116472
{txt}{space 26} {c |}
DT_score_pre_std_missing_0 {c |}{col 28}{res}{space 2} .0260043{col 40}{space 2}  .035516{col 51}{space 1}    0.73{col 60}{space 3}0.464{col 68}{space 4}-.0436059{col 81}{space 3} .0956144
{txt}{space 3}rosen_pre_std_missing_0 {c |}{col 28}{res}{space 2} .4599446{col 40}{space 2} .0295841{col 51}{space 1}   15.55{col 60}{space 3}0.000{col 68}{space 4} .4019608{col 81}{space 3} .5179284
{txt}{space 19}4.grade {c |}{col 28}{res}{space 2}-.1180539{col 40}{space 2} .1499167{col 51}{space 1}   -0.79{col 60}{space 3}0.431{col 68}{space 4}-.4118853{col 81}{space 3} .1757774
{txt}{space 22}gend {c |}{col 28}{res}{space 2} .0374923{col 40}{space 2}  .071793{col 51}{space 1}    0.52{col 60}{space 3}0.602{col 68}{space 4}-.1032195{col 81}{space 3} .1782041
{txt}{space 19}branch1 {c |}{col 28}{res}{space 2} .1244079{col 40}{space 2} .1910774{col 51}{space 1}    0.65{col 60}{space 3}0.515{col 68}{space 4} -.250097{col 81}{space 3} .4989128
{txt}{space 19}branch2 {c |}{col 28}{res}{space 2} .0434694{col 40}{space 2} .3400799{col 51}{space 1}    0.13{col 60}{space 3}0.898{col 68}{space 4} -.623075{col 81}{space 3} .7100138
{txt}{space 19}branch3 {c |}{col 28}{res}{space 2} .2548273{col 40}{space 2} .2023139{col 51}{space 1}    1.26{col 60}{space 3}0.208{col 68}{space 4}-.1417006{col 81}{space 3} .6513553
{txt}{space 12}income_source1 {c |}{col 28}{res}{space 2}-.1906322{col 40}{space 2}  .435149{col 51}{space 1}   -0.44{col 60}{space 3}0.661{col 68}{space 4}-1.043509{col 81}{space 3} .6622442
{txt}{space 12}income_source2 {c |}{col 28}{res}{space 2} .1356548{col 40}{space 2} .2840776{col 51}{space 1}    0.48{col 60}{space 3}0.633{col 68}{space 4}-.4211271{col 81}{space 3} .6924366
{txt}{space 12}income_source3 {c |}{col 28}{res}{space 2} .0213971{col 40}{space 2} .2784945{col 51}{space 1}    0.08{col 60}{space 3}0.939{col 68}{space 4} -.524442{col 81}{space 3} .5672362
{txt}{space 12}income_source4 {c |}{col 28}{res}{space 2} .0327574{col 40}{space 2} .9141124{col 51}{space 1}    0.04{col 60}{space 3}0.971{col 68}{space 4} -1.75887{col 81}{space 3} 1.824385
{txt}{space 4}last_income_per_member {c |}{col 28}{res}{space 2}-.0000223{col 40}{space 2} .0000206{col 51}{space 1}   -1.09{col 60}{space 3}0.278{col 68}{space 4}-.0000626{col 81}{space 3}  .000018
{txt}{space 18}hhmember {c |}{col 28}{res}{space 2} .0006793{col 40}{space 2} .0312772{col 51}{space 1}    0.02{col 60}{space 3}0.983{col 68}{space 4} -.060623{col 81}{space 3} .0619816
{txt}{space 17}hhheadage {c |}{col 28}{res}{space 2}-.0008471{col 40}{space 2} .0051089{col 51}{space 1}   -0.17{col 60}{space 3}0.868{col 68}{space 4}-.0108604{col 81}{space 3} .0091662
{txt}{space 13}hhheadeduyear {c |}{col 28}{res}{space 2}-.0000134{col 40}{space 2}  .010431{col 51}{space 1}   -0.00{col 60}{space 3}0.999{col 68}{space 4}-.0204579{col 81}{space 3}  .020431
{txt}{space 18}age_tchr {c |}{col 28}{res}{space 2} .0039269{col 40}{space 2} .0136631{col 51}{space 1}    0.29{col 60}{space 3}0.774{col 68}{space 4}-.0228523{col 81}{space 3}  .030706
{txt}{space 14}phone_survey {c |}{col 28}{res}{space 2}-.0318813{col 40}{space 2}  .091747{col 51}{space 1}   -0.35{col 60}{space 3}0.728{col 68}{space 4}-.2117022{col 81}{space 3} .1479395
{txt}{space 21}_cons {c |}{col 28}{res}{space 2}-.3684246{col 40}{space 2} .4473224{col 51}{space 1}   -0.82{col 60}{space 3}0.410{col 68}{space 4} -1.24516{col 81}{space 3} .5083113
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 79}Note: One or more parameters could not be estimated in 23 bootstrap replicates; standard-error estimates include only complete replications.{p_end}

{com}. matrix did_cpcs = r(table)
{txt}
{com}. scalar did_cpcs_n = e(N) / 2
{txt}
{com}. restore
{txt}
{com}. 
. // RSES
. preserve
{txt}
{com}. keep if RSES_std != .
{txt}(7 observations deleted)

{com}. rename RSES_std outcome_1
{res}{txt}
{com}. rename rosen_pre_std_missing_0 outcome_0
{res}{txt}
{com}. reshape long outcome_, i(student_no) j(time)
{txt}(j = 0 1)

Data{col 36}Wide{col 43}->{col 48}Long
{hline 77}
Number of observations     {res}         236   {txt}->   {res}472         
{txt}Number of variables        {res}       1,260   {txt}->   {res}1,260       
{txt}j variable (2 values)                     ->   {res}time
{txt}xij variables:
                    {res}outcome_0 outcome_1   {txt}->   {res}outcome_
{txt}{hline 77}

{com}. reg outcome_ i.treatment##i.time DT_score_pre_std_missing_0 cpcs_pre_std_missing_0 `controls', vce(boot, cluster(school_no) reps(1000))
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{text}Bootstrap replications ({result:1,000}){text}: {res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}10{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}20{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}30{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}{bf:x}{res}{text}.{res}{text}.{res}{text}40{res}{text}.{res}{text}{bf:x}{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}50{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}60{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}70{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}80{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}90{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}100{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}110{res}{text}.{res}{text}.{res}{text}.{res}{text}{bf:x}{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}120{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}{bf:x}{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}140{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}150{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}160{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}170{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}180{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}190{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}200{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}210{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}{bf:x}{res}{text}.{res}{text}.{res}{text}220{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}230{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}240{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}250{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}260{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}270{res}{text}.{res}{text}{bf:x}{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}280{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}290{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}300{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}310{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}320{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}330{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}340{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}350{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}360{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}370{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}380{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}{bf:x}{res}{text}.{res}{text}.{res}{text}390{res}{text}.{res}{text}.{res}{text}{bf:x}{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}400{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{text}.{res}{t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done
{res}{text}{text:{bf:x}}: Error occurred when {bf:bootstrap} executed {bf:regress}.
{res}
{txt}{col 1}Linear regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:472}
{txt}{col 57}{lalign 13:Replications}{col 70} = {res}{ralign 6:984}
{txt}{col 57}{lalign 13:Wald chi2({res:20})}{col 70} = {res}{ralign 6:260.85}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 57}{lalign 13:R-squared}{col 70} = {res}{ralign 6:0.2610}
{txt}{col 57}{lalign 13:Adj R-squared}{col 70} = {res}{ralign 6:0.2282}
{txt}{col 57}{lalign 13:Root MSE}{col 70} = {res}{ralign 6:0.8801}

{txt}{ralign 92:(Replications based on {res:33} clusters in {res:school_no})}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}   Observed{col 40}   Bootstrap{col 68}         Norm{col 81}al-based
{col 1}                  outcome_{col 28}{c |} coefficient{col 40}  std. err.{col 52}      z{col 60}   P>|z|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}1.treatment {c |}{col 28}{res}{space 2} .0135754{col 40}{space 2} .1420048{col 51}{space 1}    0.10{col 60}{space 3}0.924{col 68}{space 4}-.2647488{col 81}{space 3} .2918997
{txt}{space 20}1.time {c |}{col 28}{res}{space 2}-.1787243{col 40}{space 2}  .180647{col 51}{space 1}   -0.99{col 60}{space 3}0.322{col 68}{space 4} -.532786{col 81}{space 3} .1753374
{txt}{space 26} {c |}
{space 12}treatment#time {c |}
{space 22}1 1  {c |}{col 28}{res}{space 2} .2913969{col 40}{space 2} .2901562{col 51}{space 1}    1.00{col 60}{space 3}0.315{col 68}{space 4}-.2772988{col 81}{space 3} .8600926
{txt}{space 26} {c |}
DT_score_pre_std_missing_0 {c |}{col 28}{res}{space 2}-.0682944{col 40}{space 2} .0364452{col 51}{space 1}   -1.87{col 60}{space 3}0.061{col 68}{space 4}-.1397257{col 81}{space 3}  .003137
{txt}{space 4}cpcs_pre_std_missing_0 {c |}{col 28}{res}{space 2} .4455494{col 40}{space 2} .0367999{col 51}{space 1}   12.11{col 60}{space 3}0.000{col 68}{space 4} .3734228{col 81}{space 3} .5176759
{txt}{space 19}4.grade {c |}{col 28}{res}{space 2}-.1359092{col 40}{space 2} .1314686{col 51}{space 1}   -1.03{col 60}{space 3}0.301{col 68}{space 4}-.3935829{col 81}{space 3} .1217645
{txt}{space 22}gend {c |}{col 28}{res}{space 2}-.0038012{col 40}{space 2} .0878696{col 51}{space 1}   -0.04{col 60}{space 3}0.965{col 68}{space 4}-.1760225{col 81}{space 3} .1684201
{txt}{space 19}branch1 {c |}{col 28}{res}{space 2} .0865861{col 40}{space 2} .2035169{col 51}{space 1}    0.43{col 60}{space 3}0.671{col 68}{space 4}-.3122997{col 81}{space 3} .4854718
{txt}{space 19}branch2 {c |}{col 28}{res}{space 2} .1682456{col 40}{space 2} .2684054{col 51}{space 1}    0.63{col 60}{space 3}0.531{col 68}{space 4}-.3578194{col 81}{space 3} .6943106
{txt}{space 19}branch3 {c |}{col 28}{res}{space 2} .4378248{col 40}{space 2} .1706601{col 51}{space 1}    2.57{col 60}{space 3}0.010{col 68}{space 4} .1033371{col 81}{space 3} .7723125
{txt}{space 12}income_source1 {c |}{col 28}{res}{space 2}-.1419341{col 40}{space 2} .4474471{col 51}{space 1}   -0.32{col 60}{space 3}0.751{col 68}{space 4}-1.018914{col 81}{space 3} .7350461
{txt}{space 12}income_source2 {c |}{col 28}{res}{space 2} .1508643{col 40}{space 2} .2971954{col 51}{space 1}    0.51{col 60}{space 3}0.612{col 68}{space 4} -.431628{col 81}{space 3} .7333565
{txt}{space 12}income_source3 {c |}{col 28}{res}{space 2} .0357114{col 40}{space 2} .3042733{col 51}{space 1}    0.12{col 60}{space 3}0.907{col 68}{space 4}-.5606533{col 81}{space 3}  .632076
{txt}{space 12}income_source4 {c |}{col 28}{res}{space 2}-.0448773{col 40}{space 2} .9748164{col 51}{space 1}   -0.05{col 60}{space 3}0.963{col 68}{space 4}-1.955482{col 81}{space 3} 1.865728
{txt}{space 4}last_income_per_member {c |}{col 28}{res}{space 2}-.0000232{col 40}{space 2} .0000349{col 51}{space 1}   -0.66{col 60}{space 3}0.506{col 68}{space 4}-.0000916{col 81}{space 3} .0000452
{txt}{space 18}hhmember {c |}{col 28}{res}{space 2}-.0124573{col 40}{space 2} .0291346{col 51}{space 1}   -0.43{col 60}{space 3}0.669{col 68}{space 4}  -.06956{col 81}{space 3} .0446455
{txt}{space 17}hhheadage {c |}{col 28}{res}{space 2} .0013152{col 40}{space 2} .0056921{col 51}{space 1}    0.23{col 60}{space 3}0.817{col 68}{space 4}-.0098411{col 81}{space 3} .0124716
{txt}{space 13}hhheadeduyear {c |}{col 28}{res}{space 2}-.0097449{col 40}{space 2} .0118781{col 51}{space 1}   -0.82{col 60}{space 3}0.412{col 68}{space 4}-.0330255{col 81}{space 3} .0135357
{txt}{space 18}age_tchr {c |}{col 28}{res}{space 2} .0045532{col 40}{space 2}  .011613{col 51}{space 1}    0.39{col 60}{space 3}0.695{col 68}{space 4} -.018208{col 81}{space 3} .0273144
{txt}{space 14}phone_survey {c |}{col 28}{res}{space 2} -.016691{col 40}{space 2}  .090425{col 51}{space 1}   -0.18{col 60}{space 3}0.854{col 68}{space 4}-.1939207{col 81}{space 3} .1605388
{txt}{space 21}_cons {c |}{col 28}{res}{space 2}-.2986978{col 40}{space 2}  .435154{col 51}{space 1}   -0.69{col 60}{space 3}0.492{col 68}{space 4}-1.151584{col 81}{space 3} .5541883
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 79}Note: One or more parameters could not be estimated in 16 bootstrap replicates; standard-error estimates include only complete replications.{p_end}

{com}. matrix did_rses = r(table)
{txt}
{com}. scalar did_rses_n = e(N) / 2
{txt}
{com}. restore
{txt}
{com}. 
. 
. // significant level
. 
. local outcome cog rses cpcs
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. forvalues i = 1/30 {c -(}
{txt}  3{com}.                 if did_`dep'[4,`i']<=0.01 {c -(}
{txt}  4{com}.                         local star_`dep'_`i' %3s "***"
{txt}  5{com}.                 {c )-}
{txt}  6{com}.                 else if (did_`dep'[4,`i']>0.01) & (did_`dep'[4,`i']<=0.05) {c -(}
{txt}  7{com}.                         local star_`dep'_`i' %2s "**"
{txt}  8{com}.                 {c )-}
{txt}  9{com}.                 else if (did_`dep'[4,`i']>0.05) & (did_`dep'[4,`i']<=0.10) {c -(}
{txt} 10{com}.                         local star_`dep'_`i' %1s "*"
{txt} 11{com}.                 {c )-}
{txt} 12{com}.                 else {c -(}
{txt} 13{com}.                         local star_`dep'_`i'  ""
{txt} 14{com}.                 {c )-}
{txt} 15{com}. {c )-}
{txt} 16{com}. {c )-} 
{txt}
{com}. 
. /// Table
> tempname hh2
{txt}
{com}. file open `hh2' using "$path_output/did.tex", write replace
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "% Author: Kazuma Takakura" _newline
{txt}
{com}. file write `hh2' "% Date: `c(current_date)'" _newline
{txt}
{com}. file write `hh2' "% Time: `c(current_time)'" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. 
. file write `hh2' "\begin{c -(}table{c )-}[h!]\footnotesize" _newline
{txt}
{com}. file write `hh2' "  \centering" _newline
{txt}
{com}. file write `hh2' "  \caption{c -(}Difference in Differences{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:did{c )-}" _newline
{txt}
{com}. file write `hh2' "\scalebox{c -(}1{c )-}{c -(}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}threeparttable{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "\begin{c -(}tabular{c )-}{c -(}lccc{c )-}\toprule" _newline
{txt}
{com}. 
.   
. file write `hh2' "  & Rapid math test score &  RSES score  & CPCS score   \\\midrule\midrule" _newline
{txt}
{com}. 
. file write `hh2' "  DiD & " %04.3f (did_cog[1,8]) `star_cog_8' "  & " %04.3f (did_rses[1,8]) `star_rses_8' " & " %04.3f (did_cpcs[1,8]) `star_cpcs_8' " \\ " _newline
{txt}
{com}. file write `hh2' "    & (" %04.3f (did_cog[2,8]) ") & (" %04.3f (did_rses[2,8]) ") & (" %04.3f (did_cpcs[2,8]) ") \\ " _newline
{txt}
{com}. 
. file write `hh2' "  Treatment & " %04.3f (did_cog[1,2]) `star_cog_2' "  & " %04.3f (did_rses[1,2]) `star_rses_2' " & " %04.3f (did_cpcs[1,2]) `star_cpcs_2' " \\ " _newline
{txt}
{com}. file write `hh2' "    & (" %04.3f (did_cog[2,2]) ") & (" %04.3f (did_rses[2,2]) ") & (" %04.3f (did_cpcs[2,2]) ") \\ " _newline
{txt}
{com}. 
. file write `hh2' "  After & " %04.3f (did_cog[1,4]) `star_cog_4' "  & " %04.3f (did_rses[1,4]) `star_rses_4' " & " %04.3f (did_cpcs[1,4]) `star_cpcs_4' " \\ " _newline
{txt}
{com}. file write `hh2' "    & (" %04.3f (did_cog[2,4]) ") & (" %04.3f (did_rses[2,4]) ") & (" %04.3f (did_cpcs[2,4]) ") \\ " _newline
{txt}
{com}. 
. file write `hh2' "  Constant & " %04.3f (did_cog[1,27]) `star_cog_27' "  & " %04.3f (did_rses[1,26]) `star_rses_26' " & " %04.3f (did_cpcs[1,26]) `star_cpcs_26' " \\ " _newline
{txt}
{com}. file write `hh2' "    & (" %04.3f (did_cog[2,27]) ") & (" %04.3f (did_rses[2,26]) ") & (" %04.3f (did_cpcs[2,26]) ") \\ " _newline
{txt}
{com}. 
. file write `hh2' "  Observations & " (did_cog_n) "  & " (did_rses_n) " & " (did_cpcs_n) " \\ " _newline
{txt}
{com}. 
. file write `hh2' "\midrule" _newline
{txt}
{com}. file write `hh2' "\end{c -(}tabular{c )-}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\item (a) We control student's grade, sex, baseline cognitive and baseline non-cognitive score, DT baseline time, branch dummy (location), parents' income source, last income per family member, number of household members, age of household head, education level of household head, teacher's age, sex, and phone survey dummy. " _newline
{txt}
{com}. file write `hh2' "\item (b) School-clustered bootstrap standard errors are reported within parentheses. " _newline
{txt}
{com}. file write `hh2' "\item (c) $^*$ Significant at 10\% level; $^{c -(}**{c )-}$ significant at 5\% level; $^{c -(}***{c )-}$ significant at 1\% level. " _newline
{txt}
{com}. file write `hh2' "\end{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}threeparttable{c )-}" _newline
{txt}
{com}. file write `hh2' "{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:addlabel{c )-}%" _newline
{txt}
{com}. file write `hh2' "\end{c -(}table{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. file close `hh2'
{txt}
{com}. 
{txt}end of do-file

{com}. 
. do "$path_do/3_table_F2.do"
{txt}
{com}. * This is the do file to create "Table F2. Inverse Probability Weighted Difference in Differences"
. set seed 123
{txt}
{com}. 
. use "$path_data/temp/followup_student_parents_matched", clear
{txt}
{com}. 
. gen gend = q1d - 1
{txt}
{com}. 
. // Propensity score
. local controls i.grade gend branch1 branch2 branch3 income_source1 income_source2 income_source3 income_source4 last_income_per_member hhmember hhheadage hhheadeduyear age_tchr phone_survey
{txt}
{com}. logit treatment DT_score_pre_std_missing_0 cpcs_pre_std_missing_0 rosen_pre_std_missing_0 `controls'

{res}{txt}Iteration 0:{space 2}Log likelihood = {res:-163.86073}  
Iteration 1:{space 2}Log likelihood = {res:-139.65506}  
Iteration 2:{space 2}Log likelihood = {res:-139.28345}  
Iteration 3:{space 2}Log likelihood = {res:-139.28041}  
Iteration 4:{space 2}Log likelihood = {res:-139.28041}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:243}
{txt}{col 57}{lalign 13:LR chi2({res:18})}{col 70} = {res}{ralign 6:49.16}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0001}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-139.28041}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.1500}

{txt}{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                 treatment{col 28}{c |} Coefficient{col 40}  Std. err.{col 52}      z{col 60}   P>|z|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
DT_score_pre_std_missing_0 {c |}{col 28}{res}{space 2}-.2108634{col 40}{space 2} .1559605{col 51}{space 1}   -1.35{col 60}{space 3}0.176{col 68}{space 4}-.5165404{col 81}{space 3} .0948136
{txt}{space 4}cpcs_pre_std_missing_0 {c |}{col 28}{res}{space 2} 1.363271{col 40}{space 2}  .373272{col 51}{space 1}    3.65{col 60}{space 3}0.000{col 68}{space 4} .6316711{col 81}{space 3} 2.094871
{txt}{space 3}rosen_pre_std_missing_0 {c |}{col 28}{res}{space 2}-1.156243{col 40}{space 2} .3670308{col 51}{space 1}   -3.15{col 60}{space 3}0.002{col 68}{space 4} -1.87561{col 81}{space 3} -.436876
{txt}{space 19}4.grade {c |}{col 28}{res}{space 2} .2601036{col 40}{space 2} .3425485{col 51}{space 1}    0.76{col 60}{space 3}0.448{col 68}{space 4}-.4112792{col 81}{space 3} .9314864
{txt}{space 22}gend {c |}{col 28}{res}{space 2} .0431333{col 40}{space 2} .3136935{col 51}{space 1}    0.14{col 60}{space 3}0.891{col 68}{space 4}-.5716947{col 81}{space 3} .6579613
{txt}{space 19}branch1 {c |}{col 28}{res}{space 2}-.8730908{col 40}{space 2} .5101577{col 51}{space 1}   -1.71{col 60}{space 3}0.087{col 68}{space 4}-1.872982{col 81}{space 3} .1267998
{txt}{space 19}branch2 {c |}{col 28}{res}{space 2} .3672116{col 40}{space 2} .5857284{col 51}{space 1}    0.63{col 60}{space 3}0.531{col 68}{space 4} -.780795{col 81}{space 3} 1.515218
{txt}{space 19}branch3 {c |}{col 28}{res}{space 2}-.9819822{col 40}{space 2} .4213023{col 51}{space 1}   -2.33{col 60}{space 3}0.020{col 68}{space 4} -1.80772{col 81}{space 3}-.1562449
{txt}{space 12}income_source1 {c |}{col 28}{res}{space 2}-1.392694{col 40}{space 2} 1.198015{col 51}{space 1}   -1.16{col 60}{space 3}0.245{col 68}{space 4} -3.74076{col 81}{space 3} .9553711
{txt}{space 12}income_source2 {c |}{col 28}{res}{space 2}-.1140878{col 40}{space 2} .9307761{col 51}{space 1}   -0.12{col 60}{space 3}0.902{col 68}{space 4}-1.938375{col 81}{space 3}   1.7102
{txt}{space 12}income_source3 {c |}{col 28}{res}{space 2}-.4115209{col 40}{space 2} .8999542{col 51}{space 1}   -0.46{col 60}{space 3}0.647{col 68}{space 4}-2.175399{col 81}{space 3} 1.352357
{txt}{space 12}income_source4 {c |}{col 28}{res}{space 2} 1.910332{col 40}{space 2} 2.758545{col 51}{space 1}    0.69{col 60}{space 3}0.489{col 68}{space 4}-3.496317{col 81}{space 3} 7.316982
{txt}{space 4}last_income_per_member {c |}{col 28}{res}{space 2}-.0001351{col 40}{space 2} .0001481{col 51}{space 1}   -0.91{col 60}{space 3}0.362{col 68}{space 4}-.0004253{col 81}{space 3} .0001551
{txt}{space 18}hhmember {c |}{col 28}{res}{space 2} .1845254{col 40}{space 2} .1265051{col 51}{space 1}    1.46{col 60}{space 3}0.145{col 68}{space 4}  -.06342{col 81}{space 3} .4324708
{txt}{space 17}hhheadage {c |}{col 28}{res}{space 2}-.0072255{col 40}{space 2} .0169493{col 51}{space 1}   -0.43{col 60}{space 3}0.670{col 68}{space 4}-.0404454{col 81}{space 3} .0259945
{txt}{space 13}hhheadeduyear {c |}{col 28}{res}{space 2}-.0778674{col 40}{space 2} .0471652{col 51}{space 1}   -1.65{col 60}{space 3}0.099{col 68}{space 4}-.1703095{col 81}{space 3} .0145748
{txt}{space 18}age_tchr {c |}{col 28}{res}{space 2}-.0365818{col 40}{space 2} .0244024{col 51}{space 1}   -1.50{col 60}{space 3}0.134{col 68}{space 4}-.0844097{col 81}{space 3}  .011246
{txt}{space 14}phone_survey {c |}{col 28}{res}{space 2}-.0776531{col 40}{space 2} .3471772{col 51}{space 1}   -0.22{col 60}{space 3}0.823{col 68}{space 4}-.7581078{col 81}{space 3} .6028016
{txt}{space 21}_cons {c |}{col 28}{res}{space 2}  2.00163{col 40}{space 2} 1.433552{col 51}{space 1}    1.40{col 60}{space 3}0.163{col 68}{space 4}-.8080801{col 81}{space 3} 4.811341
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. predict pscore
{txt}(option {bf:pr} assumed; Pr(treatment))

{com}. gen weight = treatment/pscore + (1-treatment)/(1-pscore)
{txt}
{com}. 
. // Cog
. preserve
{txt}
{com}. gen outcome_1 = followup_cog_std 
{txt}
{com}. gen outcome_0 = DT_score_pre_std_missing_0
{txt}
{com}. reshape long outcome_, i(student_no) j(time)
{txt}(j = 0 1)

Data{col 36}Wide{col 43}->{col 48}Long
{hline 77}
Number of observations     {res}         243   {txt}->   {res}486         
{txt}Number of variables        {res}       1,264   {txt}->   {res}1,264       
{txt}j variable (2 values)                     ->   {res}time
{txt}xij variables:
                    {res}outcome_0 outcome_1   {txt}->   {res}outcome_
{txt}{hline 77}

{com}. gen did = treatment * time
{txt}
{com}. reg outcome_ i.treatment##i.time cpcs_pre_std_missing_0 rosen_pre_std_missing_0 `controls' [pweight=weight], cluster(school_no)
{txt}(sum of wgt is 968.097461938858)

Linear regression                               Number of obs     = {res}       486
                                                {txt}F(20, 32)         =  {res}     3.03
                                                {txt}Prob > F          = {res}    0.0026
                                                {txt}R-squared         = {res}    0.0804
                                                {txt}Root MSE          =    {res} .94462

{txt}{ralign 89:(Std. err. adjusted for {res:33} clusters in {res:school_no})}
{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}               outcome_{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}1.treatment {c |}{col 25}{res}{space 2} .0438017{col 37}{space 2} .1803927{col 48}{space 1}    0.24{col 57}{space 3}0.810{col 65}{space 4}-.3236462{col 78}{space 3} .4112496
{txt}{space 17}1.time {c |}{col 25}{res}{space 2} .2000591{col 37}{space 2} .1929213{col 48}{space 1}    1.04{col 57}{space 3}0.308{col 65}{space 4}-.1929087{col 78}{space 3}  .593027
{txt}{space 23} {c |}
{space 9}treatment#time {c |}
{space 19}1 1  {c |}{col 25}{res}{space 2}-.3230128{col 37}{space 2} .2324251{col 48}{space 1}   -1.39{col 57}{space 3}0.174{col 65}{space 4}-.7964472{col 78}{space 3} .1504215
{txt}{space 23} {c |}
{space 1}cpcs_pre_std_missing_0 {c |}{col 25}{res}{space 2} .3000759{col 37}{space 2} .1207247{col 48}{space 1}    2.49{col 57}{space 3}0.018{col 65}{space 4} .0541678{col 78}{space 3}  .545984
{txt}rosen_pre_std_missing_0 {c |}{col 25}{res}{space 2}-.2907475{col 37}{space 2}  .128463{col 48}{space 1}   -2.26{col 57}{space 3}0.031{col 65}{space 4}-.5524181{col 78}{space 3}-.0290768
{txt}{space 16}4.grade {c |}{col 25}{res}{space 2}-.1537875{col 37}{space 2}  .113061{col 48}{space 1}   -1.36{col 57}{space 3}0.183{col 65}{space 4}-.3840853{col 78}{space 3} .0765103
{txt}{space 19}gend {c |}{col 25}{res}{space 2}-.1581654{col 37}{space 2} .0889327{col 48}{space 1}   -1.78{col 57}{space 3}0.085{col 65}{space 4}-.3393153{col 78}{space 3} .0229846
{txt}{space 16}branch1 {c |}{col 25}{res}{space 2} -.007055{col 37}{space 2}  .152365{col 48}{space 1}   -0.05{col 57}{space 3}0.963{col 65}{space 4}-.3174122{col 78}{space 3} .3033023
{txt}{space 16}branch2 {c |}{col 25}{res}{space 2} .0034903{col 37}{space 2} .1289986{col 48}{space 1}    0.03{col 57}{space 3}0.979{col 65}{space 4}-.2592713{col 78}{space 3} .2662519
{txt}{space 16}branch3 {c |}{col 25}{res}{space 2}-.1517692{col 37}{space 2} .1303231{col 48}{space 1}   -1.16{col 57}{space 3}0.253{col 65}{space 4}-.4172287{col 78}{space 3} .1136903
{txt}{space 9}income_source1 {c |}{col 25}{res}{space 2}-.0622228{col 37}{space 2} .3134014{col 48}{space 1}   -0.20{col 57}{space 3}0.844{col 65}{space 4}-.7006007{col 78}{space 3}  .576155
{txt}{space 9}income_source2 {c |}{col 25}{res}{space 2}-.0777042{col 37}{space 2} .2495967{col 48}{space 1}   -0.31{col 57}{space 3}0.758{col 65}{space 4} -.586116{col 78}{space 3} .4307076
{txt}{space 9}income_source3 {c |}{col 25}{res}{space 2} -.143434{col 37}{space 2} .2210532{col 48}{space 1}   -0.65{col 57}{space 3}0.521{col 65}{space 4}-.5937047{col 78}{space 3} .3068367
{txt}{space 9}income_source4 {c |}{col 25}{res}{space 2} .2814515{col 37}{space 2} .7308102{col 48}{space 1}    0.39{col 57}{space 3}0.703{col 65}{space 4} -1.20716{col 78}{space 3} 1.770063
{txt}{space 1}last_income_per_member {c |}{col 25}{res}{space 2} .0000406{col 37}{space 2} .0000304{col 48}{space 1}    1.34{col 57}{space 3}0.191{col 65}{space 4}-.0000213{col 78}{space 3} .0001025
{txt}{space 15}hhmember {c |}{col 25}{res}{space 2} -.082263{col 37}{space 2} .0352219{col 48}{space 1}   -2.34{col 57}{space 3}0.026{col 65}{space 4}-.1540076{col 78}{space 3}-.0105184
{txt}{space 14}hhheadage {c |}{col 25}{res}{space 2}-.0046053{col 37}{space 2} .0059524{col 48}{space 1}   -0.77{col 57}{space 3}0.445{col 65}{space 4}  -.01673{col 78}{space 3} .0075194
{txt}{space 10}hhheadeduyear {c |}{col 25}{res}{space 2} .0039424{col 37}{space 2} .0142118{col 48}{space 1}    0.28{col 57}{space 3}0.783{col 65}{space 4} -.025006{col 78}{space 3} .0328908
{txt}{space 15}age_tchr {c |}{col 25}{res}{space 2}-.0019182{col 37}{space 2} .0066594{col 48}{space 1}   -0.29{col 57}{space 3}0.775{col 65}{space 4} -.015483{col 78}{space 3} .0116465
{txt}{space 11}phone_survey {c |}{col 25}{res}{space 2} .2085334{col 37}{space 2} .1220806{col 48}{space 1}    1.71{col 57}{space 3}0.097{col 65}{space 4}-.0401367{col 78}{space 3} .4572034
{txt}{space 18}_cons {c |}{col 25}{res}{space 2} .8396089{col 37}{space 2} .5262866{col 48}{space 1}    1.60{col 57}{space 3}0.120{col 65}{space 4}-.2324018{col 78}{space 3}  1.91162
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. matrix did_cog = r(table)
{txt}
{com}. scalar did_cog_n = e(N) / 2
{txt}
{com}. restore
{txt}
{com}. 
. // CPCS
. preserve
{txt}
{com}. keep if CPCS_std != .
{txt}(7 observations deleted)

{com}. gen outcome_1 = CPCS_std 
{txt}
{com}. gen outcome_0 = cpcs_pre_std_missing_0 
{txt}
{com}. reshape long outcome_, i(student_no) j(time)
{txt}(j = 0 1)

Data{col 36}Wide{col 43}->{col 48}Long
{hline 77}
Number of observations     {res}         236   {txt}->   {res}472         
{txt}Number of variables        {res}       1,264   {txt}->   {res}1,264       
{txt}j variable (2 values)                     ->   {res}time
{txt}xij variables:
                    {res}outcome_0 outcome_1   {txt}->   {res}outcome_
{txt}{hline 77}

{com}. reg outcome_ i.treatment##i.time DT_score_pre_std_missing_0 rosen_pre_std_missing_0 `controls' [pweight=weight], cluster(school_no)
{txt}(sum of wgt is 937.3212029933929)

Linear regression                               Number of obs     = {res}       472
                                                {txt}F(20, 32)         =  {res}    47.91
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.3055
                                                {txt}Root MSE          =    {res} .89151

{txt}{ralign 92:(Std. err. adjusted for {res:33} clusters in {res:school_no})}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}                  outcome_{col 28}{c |} Coefficient{col 40}  std. err.{col 52}      t{col 60}   P>|t|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}1.treatment {c |}{col 28}{res}{space 2} -.034079{col 40}{space 2} .1474007{col 51}{space 1}   -0.23{col 60}{space 3}0.819{col 68}{space 4}-.3343244{col 81}{space 3} .2661664
{txt}{space 20}1.time {c |}{col 28}{res}{space 2}-.3569982{col 40}{space 2} .2294761{col 51}{space 1}   -1.56{col 60}{space 3}0.130{col 68}{space 4}-.8244258{col 81}{space 3} .1104294
{txt}{space 26} {c |}
{space 12}treatment#time {c |}
{space 22}1 1  {c |}{col 28}{res}{space 2} .5958291{col 40}{space 2} .3327545{col 51}{space 1}    1.79{col 60}{space 3}0.083{col 68}{space 4}-.0819697{col 81}{space 3} 1.273628
{txt}{space 26} {c |}
DT_score_pre_std_missing_0 {c |}{col 28}{res}{space 2} .0222761{col 40}{space 2} .0389386{col 51}{space 1}    0.57{col 60}{space 3}0.571{col 68}{space 4}-.0570392{col 81}{space 3} .1015914
{txt}{space 3}rosen_pre_std_missing_0 {c |}{col 28}{res}{space 2} .4792491{col 40}{space 2} .0258206{col 51}{space 1}   18.56{col 60}{space 3}0.000{col 68}{space 4} .4266543{col 81}{space 3}  .531844
{txt}{space 19}4.grade {c |}{col 28}{res}{space 2} -.130848{col 40}{space 2} .0922791{col 51}{space 1}   -1.42{col 60}{space 3}0.166{col 68}{space 4}-.3188144{col 81}{space 3} .0571185
{txt}{space 22}gend {c |}{col 28}{res}{space 2} .0450206{col 40}{space 2} .0817841{col 51}{space 1}    0.55{col 60}{space 3}0.586{col 68}{space 4}-.1215681{col 81}{space 3} .2116094
{txt}{space 19}branch1 {c |}{col 28}{res}{space 2}-.0014069{col 40}{space 2} .1289253{col 51}{space 1}   -0.01{col 60}{space 3}0.991{col 68}{space 4}-.2640192{col 81}{space 3} .2612053
{txt}{space 19}branch2 {c |}{col 28}{res}{space 2}-.0412931{col 40}{space 2} .2277868{col 51}{space 1}   -0.18{col 60}{space 3}0.857{col 68}{space 4}-.5052797{col 81}{space 3} .4226935
{txt}{space 19}branch3 {c |}{col 28}{res}{space 2} .1676179{col 40}{space 2} .1613224{col 51}{space 1}    1.04{col 60}{space 3}0.307{col 68}{space 4}-.1609852{col 81}{space 3}  .496221
{txt}{space 12}income_source1 {c |}{col 28}{res}{space 2}-.0230177{col 40}{space 2} .4216357{col 51}{space 1}   -0.05{col 60}{space 3}0.957{col 68}{space 4}-.8818614{col 81}{space 3} .8358261
{txt}{space 12}income_source2 {c |}{col 28}{res}{space 2} .2229558{col 40}{space 2} .2472723{col 51}{space 1}    0.90{col 60}{space 3}0.374{col 68}{space 4}-.2807213{col 81}{space 3} .7266329
{txt}{space 12}income_source3 {c |}{col 28}{res}{space 2} .1321402{col 40}{space 2} .2436682{col 51}{space 1}    0.54{col 60}{space 3}0.591{col 68}{space 4}-.3641956{col 81}{space 3} .6284761
{txt}{space 12}income_source4 {c |}{col 28}{res}{space 2}-.3321425{col 40}{space 2} .8190184{col 51}{space 1}   -0.41{col 60}{space 3}0.688{col 68}{space 4}-2.000428{col 81}{space 3} 1.336144
{txt}{space 4}last_income_per_member {c |}{col 28}{res}{space 2}-.0000184{col 40}{space 2}  .000012{col 51}{space 1}   -1.54{col 60}{space 3}0.134{col 68}{space 4}-.0000427{col 81}{space 3} 5.96e-06
{txt}{space 18}hhmember {c |}{col 28}{res}{space 2} .0192559{col 40}{space 2} .0308939{col 51}{space 1}    0.62{col 60}{space 3}0.538{col 68}{space 4} -.043673{col 81}{space 3} .0821848
{txt}{space 17}hhheadage {c |}{col 28}{res}{space 2} .0007508{col 40}{space 2} .0049955{col 51}{space 1}    0.15{col 60}{space 3}0.881{col 68}{space 4}-.0094247{col 81}{space 3} .0109262
{txt}{space 13}hhheadeduyear {c |}{col 28}{res}{space 2} .0067517{col 40}{space 2}  .009207{col 51}{space 1}    0.73{col 60}{space 3}0.469{col 68}{space 4}-.0120022{col 81}{space 3} .0255057
{txt}{space 18}age_tchr {c |}{col 28}{res}{space 2} .0030969{col 40}{space 2} .0091549{col 51}{space 1}    0.34{col 60}{space 3}0.737{col 68}{space 4}-.0155511{col 81}{space 3} .0217448
{txt}{space 14}phone_survey {c |}{col 28}{res}{space 2}-.0248752{col 40}{space 2} .0946675{col 51}{space 1}   -0.26{col 60}{space 3}0.794{col 68}{space 4}-.2177066{col 81}{space 3} .1679563
{txt}{space 21}_cons {c |}{col 28}{res}{space 2}-.3035289{col 40}{space 2} .4052393{col 51}{space 1}   -0.75{col 60}{space 3}0.459{col 68}{space 4}-1.128974{col 81}{space 3} .5219166
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. matrix did_cpcs = r(table)
{txt}
{com}. scalar did_cpcs_n = e(N) / 2
{txt}
{com}. restore
{txt}
{com}. 
. // RSES
. preserve
{txt}
{com}. keep if RSES_std != .
{txt}(7 observations deleted)

{com}. gen outcome_1 = RSES_std
{txt}
{com}. gen outcome_0 = rosen_pre_std_missing_0
{txt}
{com}. reshape long outcome_, i(student_no) j(time)
{txt}(j = 0 1)

Data{col 36}Wide{col 43}->{col 48}Long
{hline 77}
Number of observations     {res}         236   {txt}->   {res}472         
{txt}Number of variables        {res}       1,264   {txt}->   {res}1,264       
{txt}j variable (2 values)                     ->   {res}time
{txt}xij variables:
                    {res}outcome_0 outcome_1   {txt}->   {res}outcome_
{txt}{hline 77}

{com}. reg outcome_ i.treatment##i.time DT_score_pre_std_missing_0 cpcs_pre_std_missing_0 `controls' [pweight=weight], cluster(school_no)
{txt}(sum of wgt is 937.3212029933929)

Linear regression                               Number of obs     = {res}       472
                                                {txt}F(20, 32)         =  {res}    29.79
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2835
                                                {txt}Root MSE          =    {res} .89578

{txt}{ralign 92:(Std. err. adjusted for {res:33} clusters in {res:school_no})}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}                  outcome_{col 28}{c |} Coefficient{col 40}  std. err.{col 52}      t{col 60}   P>|t|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}1.treatment {c |}{col 28}{res}{space 2}-.0561191{col 40}{space 2} .1565546{col 51}{space 1}   -0.36{col 60}{space 3}0.722{col 68}{space 4}-.3750104{col 81}{space 3} .2627723
{txt}{space 20}1.time {c |}{col 28}{res}{space 2} -.356269{col 40}{space 2} .2438618{col 51}{space 1}   -1.46{col 60}{space 3}0.154{col 68}{space 4}-.8529991{col 81}{space 3} .1404611
{txt}{space 26} {c |}
{space 12}treatment#time {c |}
{space 22}1 1  {c |}{col 28}{res}{space 2} .5857784{col 40}{space 2} .3426854{col 51}{space 1}    1.71{col 60}{space 3}0.097{col 68}{space 4}-.1122489{col 81}{space 3} 1.283806
{txt}{space 26} {c |}
DT_score_pre_std_missing_0 {c |}{col 28}{res}{space 2}   -.0643{col 40}{space 2} .0372471{col 51}{space 1}   -1.73{col 60}{space 3}0.094{col 68}{space 4}-.1401698{col 81}{space 3} .0115699
{txt}{space 4}cpcs_pre_std_missing_0 {c |}{col 28}{res}{space 2} .4294754{col 40}{space 2} .0339431{col 51}{space 1}   12.65{col 60}{space 3}0.000{col 68}{space 4} .3603356{col 81}{space 3} .4986152
{txt}{space 19}4.grade {c |}{col 28}{res}{space 2}-.1608095{col 40}{space 2} .0942293{col 51}{space 1}   -1.71{col 60}{space 3}0.098{col 68}{space 4}-.3527482{col 81}{space 3} .0311293
{txt}{space 22}gend {c |}{col 28}{res}{space 2} .0135893{col 40}{space 2} .0972795{col 51}{space 1}    0.14{col 60}{space 3}0.890{col 68}{space 4}-.1845625{col 81}{space 3} .2117411
{txt}{space 19}branch1 {c |}{col 28}{res}{space 2} .0342147{col 40}{space 2} .1535465{col 51}{space 1}    0.22{col 60}{space 3}0.825{col 68}{space 4}-.2785493{col 81}{space 3} .3469788
{txt}{space 19}branch2 {c |}{col 28}{res}{space 2} .0664316{col 40}{space 2} .2084987{col 51}{space 1}    0.32{col 60}{space 3}0.752{col 68}{space 4}-.3582663{col 81}{space 3} .4911295
{txt}{space 19}branch3 {c |}{col 28}{res}{space 2} .4003525{col 40}{space 2} .1644129{col 51}{space 1}    2.44{col 60}{space 3}0.021{col 68}{space 4} .0654545{col 81}{space 3} .7352505
{txt}{space 12}income_source1 {c |}{col 28}{res}{space 2}-.0629617{col 40}{space 2} .3953778{col 51}{space 1}   -0.16{col 60}{space 3}0.874{col 68}{space 4}  -.86832{col 81}{space 3} .7423966
{txt}{space 12}income_source2 {c |}{col 28}{res}{space 2} .1794668{col 40}{space 2} .2452292{col 51}{space 1}    0.73{col 60}{space 3}0.470{col 68}{space 4}-.3200487{col 81}{space 3} .6789823
{txt}{space 12}income_source3 {c |}{col 28}{res}{space 2} .1161484{col 40}{space 2} .2549179{col 51}{space 1}    0.46{col 60}{space 3}0.652{col 68}{space 4}-.4031023{col 81}{space 3} .6353991
{txt}{space 12}income_source4 {c |}{col 28}{res}{space 2}-.2324992{col 40}{space 2} .8155409{col 51}{space 1}   -0.29{col 60}{space 3}0.777{col 68}{space 4}-1.893702{col 81}{space 3} 1.428703
{txt}{space 4}last_income_per_member {c |}{col 28}{res}{space 2}-.0000118{col 40}{space 2} .0000218{col 51}{space 1}   -0.54{col 60}{space 3}0.593{col 68}{space 4}-.0000561{col 81}{space 3} .0000326
{txt}{space 18}hhmember {c |}{col 28}{res}{space 2} .0026131{col 40}{space 2} .0271617{col 51}{space 1}    0.10{col 60}{space 3}0.924{col 68}{space 4}-.0527136{col 81}{space 3} .0579397
{txt}{space 17}hhheadage {c |}{col 28}{res}{space 2} .0014542{col 40}{space 2} .0056246{col 51}{space 1}    0.26{col 60}{space 3}0.798{col 68}{space 4}-.0100029{col 81}{space 3} .0129112
{txt}{space 13}hhheadeduyear {c |}{col 28}{res}{space 2}-.0026636{col 40}{space 2} .0127576{col 51}{space 1}   -0.21{col 60}{space 3}0.836{col 68}{space 4}-.0286499{col 81}{space 3} .0233227
{txt}{space 18}age_tchr {c |}{col 28}{res}{space 2} .0023072{col 40}{space 2} .0088969{col 51}{space 1}    0.26{col 60}{space 3}0.797{col 68}{space 4}-.0158152{col 81}{space 3} .0204296
{txt}{space 14}phone_survey {c |}{col 28}{res}{space 2}-.0009133{col 40}{space 2} .1024838{col 51}{space 1}   -0.01{col 60}{space 3}0.993{col 68}{space 4} -.209666{col 81}{space 3} .2078395
{txt}{space 21}_cons {c |}{col 28}{res}{space 2}-.2766251{col 40}{space 2} .3999211{col 51}{space 1}   -0.69{col 60}{space 3}0.494{col 68}{space 4}-1.091238{col 81}{space 3} .5379875
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. matrix did_rses = r(table)
{txt}
{com}. scalar did_rses_n = e(N) / 2
{txt}
{com}. restore
{txt}
{com}. 
. 
. // significant level
. 
. local outcome cog rses cpcs
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. forvalues i = 1/26 {c -(}
{txt}  3{com}.                 if did_`dep'[4,`i']<=0.01 {c -(}
{txt}  4{com}.                         local star_`dep'_`i' %3s "***"
{txt}  5{com}.                 {c )-}
{txt}  6{com}.                 else if (did_`dep'[4,`i']>0.01) & (did_`dep'[4,`i']<=0.05) {c -(}
{txt}  7{com}.                         local star_`dep'_`i' %2s "**"
{txt}  8{com}.                 {c )-}
{txt}  9{com}.                 else if (did_`dep'[4,`i']>0.05) & (did_`dep'[4,`i']<=0.10) {c -(}
{txt} 10{com}.                         local star_`dep'_`i' %1s "*"
{txt} 11{com}.                 {c )-}
{txt} 12{com}.                 else {c -(}
{txt} 13{com}.                         local star_`dep'_`i'  ""
{txt} 14{com}.                 {c )-}
{txt} 15{com}. {c )-}
{txt} 16{com}. {c )-} 
{txt}
{com}. 
. /// Table
> tempname hh2
{txt}
{com}. file open `hh2' using "$path_output/did_ipw.tex", write replace
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "% Author: Kazuma Takakura" _newline
{txt}
{com}. file write `hh2' "% Date: `c(current_date)'" _newline
{txt}
{com}. file write `hh2' "% Time: `c(current_time)'" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. 
. file write `hh2' "\begin{c -(}table{c )-}[h!]\footnotesize" _newline
{txt}
{com}. file write `hh2' "  \centering" _newline
{txt}
{com}. file write `hh2' "  \caption{c -(}Inverse Probability Weighted Difference in Differences{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:did_ipw{c )-}" _newline
{txt}
{com}. file write `hh2' "\scalebox{c -(}1{c )-}{c -(}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}threeparttable{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "\begin{c -(}tabular{c )-}{c -(}lccc{c )-}\toprule" _newline
{txt}
{com}. 
.   
. file write `hh2' "  & Rapid math test score &  RSES score  & CPCS score   \\\midrule\midrule" _newline
{txt}
{com}. 
. file write `hh2' "  DiD & " %04.3f (did_cog[1,8]) `star_cog_8' "  & " %04.3f (did_rses[1,8]) `star_rses_8' " & " %04.3f (did_cpcs[1,8]) `star_cpcs_8' " \\ " _newline
{txt}
{com}. file write `hh2' "    & (" %04.3f (did_cog[2,8]) ") & (" %04.3f (did_rses[2,8]) ") & (" %04.3f (did_cpcs[2,8]) ") \\ " _newline
{txt}
{com}. 
. file write `hh2' "  Treatment & " %04.3f (did_cog[1,2]) `star_cog_2' "  & " %04.3f (did_rses[1,2]) `star_rses_2' " & " %04.3f (did_cpcs[1,2]) `star_cpcs_2' " \\ " _newline
{txt}
{com}. file write `hh2' "    & (" %04.3f (did_cog[2,2]) ") & (" %04.3f (did_rses[2,2]) ") & (" %04.3f (did_cpcs[2,2]) ") \\ " _newline
{txt}
{com}. 
. file write `hh2' "  After & " %04.3f (did_cog[1,4]) `star_cog_4' "  & " %04.3f (did_rses[1,4]) `star_rses_4' " & " %04.3f (did_cpcs[1,4]) `star_cpcs_4' " \\ " _newline
{txt}
{com}. file write `hh2' "    & (" %04.3f (did_cog[2,4]) ") & (" %04.3f (did_rses[2,4]) ") & (" %04.3f (did_cpcs[2,4]) ") \\ " _newline
{txt}
{com}. 
. file write `hh2' "  Constant & " %04.3f (did_cog[1,26]) `star_cog_26' "  & " %04.3f (did_rses[1,26]) `star_rses_26' " & " %04.3f (did_cpcs[1,26]) `star_cpcs_26' " \\ " _newline
{txt}
{com}. file write `hh2' "    & (" %04.3f (did_cog[2,26]) ") & (" %04.3f (did_rses[2,26]) ") & (" %04.3f (did_cpcs[2,26]) ") \\ " _newline
{txt}
{com}. 
. file write `hh2' "  Observations & " (did_cog_n) "  & " (did_rses_n) " & " (did_cpcs_n) " \\ " _newline
{txt}
{com}. 
. file write `hh2' "\midrule" _newline
{txt}
{com}. file write `hh2' "\end{c -(}tabular{c )-}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\item (a) We control student's grade, sex, baseline cognitive and baseline non-cognitive score, DT baseline time, branch dummy (location), parents' income source, last income per family member, number of household members, age of household head, education level of household head, teacher's age, sex, and phone survey dummy. The same variables are used for propensity score calculation." _newline
{txt}
{com}. file write `hh2' "\item (b) School-clustered standard errors are reported within parentheses. " _newline
{txt}
{com}. file write `hh2' "\item (c) $^*$ Significant at 10\% level; $^{c -(}**{c )-}$ significant at 5\% level; $^{c -(}***{c )-}$ significant at 1\% level. " _newline
{txt}
{com}. file write `hh2' "\end{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}threeparttable{c )-}" _newline
{txt}
{com}. file write `hh2' "{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:addlabel{c )-}%" _newline
{txt}
{com}. file write `hh2' "\end{c -(}table{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. file close `hh2'
{txt}
{com}. 
{txt}end of do-file

{com}. 
. do "$path_do/3_table_F3.do"
{txt}
{com}. * This is the do file to create "Table F3. Difference in Differences (Matched Sample)"
. set seed 123
{txt}
{com}. 
. use "$path_data/temp/followup_student_parents_matched", clear
{txt}
{com}. 
. gen gend = q1d - 1
{txt}
{com}. 
. local controls i.grade gend branch1 branch2 branch3 income_source1 income_source2 income_source3 income_source4 last_income_per_member hhmember hhheadage hhheadeduyear age_tchr phone_survey
{txt}
{com}. teffects psmatch (followup_cog_std) (treatment DT_score_pre_std_missing_0 cpcs_pre_std_missing_0 rosen_pre_std_missing_0 `controls') 
{res}
{txt}Treatment-effects estimation{col 48}Number of obs {col 67}= {res}       243
{txt:Estimator}{col 16}:{res: propensity-score matching}{col 48}{txt:Matches: requested }{col 67}{txt:=}          1
{txt:Outcome model}{col 16}:{res: matching}{txt}{col 63}min {col 67}= {res}         1
{txt:Treatment model}{col 16}:{res: logit}{col 63}{txt:max }{col 67}{txt:=}          1
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}   AI robust
{col 1}followup_c~d{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ATE          {txt}{c |}
{space 3}treatment {c |}
{space 3}(1 vs 0)  {c |}{col 14}{res}{space 2}-.2234479{col 26}{space 2} .1415597{col 37}{space 1}   -1.58{col 46}{space 3}0.114{col 54}{space 4}-.5008998{col 67}{space 3} .0540039
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. psmatch2 treatment `controls', outcome(followup_cog_std) noreplacement
{res}
{txt}{col 1}Probit regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:243}
{txt}{col 57}{lalign 13:LR chi2({res:15})}{col 70} = {res}{ralign 6:34.58}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0028}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 9:-146.5686}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.1055}

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}             treatment{col 24}{c |} Coefficient{col 36}  Std. err.{col 48}      z{col 56}   P>|z|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}4.grade {c |}{col 24}{res}{space 2}    .1615{col 36}{space 2}  .199106{col 47}{space 1}    0.81{col 56}{space 3}0.417{col 64}{space 4}-.2287407{col 77}{space 3} .5517406
{txt}{space 18}gend {c |}{col 24}{res}{space 2} .0762294{col 36}{space 2} .1805964{col 47}{space 1}    0.42{col 56}{space 3}0.673{col 64}{space 4} -.277733{col 77}{space 3} .4301919
{txt}{space 15}branch1 {c |}{col 24}{res}{space 2}-.6131427{col 36}{space 2} .2913104{col 47}{space 1}   -2.10{col 56}{space 3}0.035{col 64}{space 4}-1.184101{col 77}{space 3}-.0421848
{txt}{space 15}branch2 {c |}{col 24}{res}{space 2} .0633647{col 36}{space 2} .3409269{col 47}{space 1}    0.19{col 56}{space 3}0.853{col 64}{space 4}-.6048398{col 77}{space 3} .7315691
{txt}{space 15}branch3 {c |}{col 24}{res}{space 2}-.7760038{col 36}{space 2} .2437862{col 47}{space 1}   -3.18{col 56}{space 3}0.001{col 64}{space 4}-1.253816{col 77}{space 3}-.2981916
{txt}{space 8}income_source1 {c |}{col 24}{res}{space 2}-.8678153{col 36}{space 2} .7131824{col 47}{space 1}   -1.22{col 56}{space 3}0.224{col 64}{space 4}-2.265627{col 77}{space 3} .5299964
{txt}{space 8}income_source2 {c |}{col 24}{res}{space 2}-.0705556{col 36}{space 2} .5618473{col 47}{space 1}   -0.13{col 56}{space 3}0.900{col 64}{space 4}-1.171756{col 77}{space 3} 1.030645
{txt}{space 8}income_source3 {c |}{col 24}{res}{space 2}-.2605725{col 36}{space 2} .5411181{col 47}{space 1}   -0.48{col 56}{space 3}0.630{col 64}{space 4}-1.321145{col 77}{space 3} .7999995
{txt}{space 8}income_source4 {c |}{col 24}{res}{space 2} 1.193503{col 36}{space 2} 1.660674{col 47}{space 1}    0.72{col 56}{space 3}0.472{col 64}{space 4}-2.061357{col 77}{space 3} 4.448364
{txt}last_income_per_member {c |}{col 24}{res}{space 2}-.0001101{col 36}{space 2} .0000925{col 47}{space 1}   -1.19{col 56}{space 3}0.234{col 64}{space 4}-.0002915{col 77}{space 3} .0000712
{txt}{space 14}hhmember {c |}{col 24}{res}{space 2} .1039315{col 36}{space 2} .0728872{col 47}{space 1}    1.43{col 56}{space 3}0.154{col 64}{space 4}-.0389248{col 77}{space 3} .2467879
{txt}{space 13}hhheadage {c |}{col 24}{res}{space 2}-.0046784{col 36}{space 2} .0099531{col 47}{space 1}   -0.47{col 56}{space 3}0.638{col 64}{space 4}-.0241861{col 77}{space 3} .0148292
{txt}{space 9}hhheadeduyear {c |}{col 24}{res}{space 2} -.046044{col 36}{space 2} .0274486{col 47}{space 1}   -1.68{col 56}{space 3}0.093{col 64}{space 4}-.0998422{col 77}{space 3} .0077542
{txt}{space 14}age_tchr {c |}{col 24}{res}{space 2}-.0242375{col 36}{space 2} .0143822{col 47}{space 1}   -1.69{col 56}{space 3}0.092{col 64}{space 4}-.0524262{col 77}{space 3} .0039511
{txt}{space 10}phone_survey {c |}{col 24}{res}{space 2}-.0365591{col 36}{space 2} .2042384{col 47}{space 1}   -0.18{col 56}{space 3}0.858{col 64}{space 4}-.4368591{col 77}{space 3} .3637408
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} 1.423968{col 36}{space 2} .8425599{col 47}{space 1}    1.69{col 56}{space 3}0.091{col 64}{space 4}-.2274193{col 77}{space 3} 3.075355
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}{hline 28}{c TT}{hline 59}
        Variable     Sample {c |}    Treated     Controls   Difference         S.E.   T-stat
{hline 28}{c +}{hline 59}
followup_cog_std  Unmatched {c |}{res}-.092040852   .136183089  -.228223941   .130213149    -1.75
{txt}{col 17}        ATT {c |}{res}-.209204736   .136183089  -.345387825   .139668944    -2.47
{txt}{hline 28}{c +}{hline 59}
Note: S.E. does not take into account that the propensity score is estimated.

 psmatch2: {c |}   psmatch2: Common
 Treatment {c |}        support
assignment {c |} Off suppo  On suppor {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
 Untreated {c |}{res}         0         98 {txt}{c |}{res}        98 
{txt}   Treated {c |}{res}        47         98 {txt}{c |}{res}       145 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}        47        196 {txt}{c |}{res}       243 
{txt}
{com}. gen psmattrition = 1 if _support!=1
{txt}(196 missing values generated)

{com}. recode psmattrition (.=0)
{txt}(196 changes made to {bf:psmattrition})

{com}. keep if psmattrition == 0
{txt}(47 observations deleted)

{com}. 
. // Cog
. preserve
{txt}
{com}. gen outcome_1 = followup_cog_std 
{txt}
{com}. gen outcome_0 = DT_score_pre_std_missing_0
{txt}
{com}. reshape long outcome_, i(student_no) j(time)
{txt}(j = 0 1)

Data{col 36}Wide{col 43}->{col 48}Long
{hline 77}
Number of observations     {res}         196   {txt}->   {res}392         
{txt}Number of variables        {res}       1,271   {txt}->   {res}1,271       
{txt}j variable (2 values)                     ->   {res}time
{txt}xij variables:
                    {res}outcome_0 outcome_1   {txt}->   {res}outcome_
{txt}{hline 77}

{com}. reg outcome_ i.treatment##i.time cpcs_pre_std_missing_0 rosen_pre_std_missing_0 `controls', vce(boot, cluster(school_no) reps(1000))
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{text}Bootstrap replications ({result:1,000}){text}: 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done
{res}{text}{text:{bf:x}}: Error occurred when {bf:bootstrap} executed {bf:regress}.
{res}
{txt}{col 1}Linear regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:392}
{txt}{col 57}{lalign 13:Replications}{col 70} = {res}{ralign 6:968}
{txt}{col 57}{lalign 13:Wald chi2({res:20})}{col 70} = {res}{ralign 6:26.51}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.1495}
{txt}{col 57}{lalign 13:R-squared}{col 70} = {res}{ralign 6:0.0983}
{txt}{col 57}{lalign 13:Adj R-squared}{col 70} = {res}{ralign 6:0.0496}
{txt}{col 57}{lalign 13:Root MSE}{col 70} = {res}{ralign 6:0.9620}

{txt}{ralign 89:(Replications based on {res:32} clusters in {res:school_no})}
{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}   Observed{col 37}   Bootstrap{col 65}         Norm{col 78}al-based
{col 1}               outcome_{col 25}{c |} coefficient{col 37}  std. err.{col 49}      z{col 57}   P>|z|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}1.treatment {c |}{col 25}{res}{space 2}-.2549605{col 37}{space 2} .2304514{col 48}{space 1}   -1.11{col 57}{space 3}0.269{col 65}{space 4}-.7066369{col 78}{space 3} .1967159
{txt}{space 17}1.time {c |}{col 25}{res}{space 2} .0901164{col 37}{space 2}  .184384{col 48}{space 1}    0.49{col 57}{space 3}0.625{col 65}{space 4}-.2712696{col 78}{space 3} .4515023
{txt}{space 23} {c |}
{space 9}treatment#time {c |}
{space 19}1 1  {c |}{col 25}{res}{space 2}-.1880976{col 37}{space 2} .2307932{col 48}{space 1}   -0.82{col 57}{space 3}0.415{col 65}{space 4} -.640444{col 78}{space 3} .2642489
{txt}{space 23} {c |}
{space 1}cpcs_pre_std_missing_0 {c |}{col 25}{res}{space 2} .3746183{col 37}{space 2}  .127209{col 48}{space 1}    2.94{col 57}{space 3}0.003{col 65}{space 4} .1252933{col 78}{space 3} .6239433
{txt}rosen_pre_std_missing_0 {c |}{col 25}{res}{space 2} -.344272{col 37}{space 2} .1338804{col 48}{space 1}   -2.57{col 57}{space 3}0.010{col 65}{space 4}-.6066726{col 78}{space 3}-.0818713
{txt}{space 16}4.grade {c |}{col 25}{res}{space 2}-.2892497{col 37}{space 2} .1764486{col 48}{space 1}   -1.64{col 57}{space 3}0.101{col 65}{space 4}-.6350826{col 78}{space 3} .0565832
{txt}{space 19}gend {c |}{col 25}{res}{space 2}-.1109102{col 37}{space 2} .1059684{col 48}{space 1}   -1.05{col 57}{space 3}0.295{col 65}{space 4}-.3186045{col 78}{space 3}  .096784
{txt}{space 16}branch1 {c |}{col 25}{res}{space 2} .2114663{col 37}{space 2} .2871669{col 48}{space 1}    0.74{col 57}{space 3}0.461{col 65}{space 4}-.3513705{col 78}{space 3}  .774303
{txt}{space 16}branch2 {c |}{col 25}{res}{space 2} .0683726{col 37}{space 2} .2646399{col 48}{space 1}    0.26{col 57}{space 3}0.796{col 65}{space 4} -.450312{col 78}{space 3} .5870572
{txt}{space 16}branch3 {c |}{col 25}{res}{space 2} .0122117{col 37}{space 2} .2115664{col 48}{space 1}    0.06{col 57}{space 3}0.954{col 65}{space 4}-.4024509{col 78}{space 3} .4268743
{txt}{space 9}income_source1 {c |}{col 25}{res}{space 2}-.0046913{col 37}{space 2} .4401373{col 48}{space 1}   -0.01{col 57}{space 3}0.991{col 65}{space 4}-.8673446{col 78}{space 3}  .857962
{txt}{space 9}income_source2 {c |}{col 25}{res}{space 2}-.0967542{col 37}{space 2} .3654743{col 48}{space 1}   -0.26{col 57}{space 3}0.791{col 65}{space 4}-.8130706{col 78}{space 3} .6195622
{txt}{space 9}income_source3 {c |}{col 25}{res}{space 2}-.0379191{col 37}{space 2} .3370309{col 48}{space 1}   -0.11{col 57}{space 3}0.910{col 65}{space 4}-.6984875{col 78}{space 3} .6226493
{txt}{space 9}income_source4 {c |}{col 25}{res}{space 2} .1391932{col 37}{space 2} 1.086451{col 48}{space 1}    0.13{col 57}{space 3}0.898{col 65}{space 4}-1.990212{col 78}{space 3} 2.268598
{txt}{space 1}last_income_per_member {c |}{col 25}{res}{space 2} .0000299{col 37}{space 2}  .000052{col 48}{space 1}    0.58{col 57}{space 3}0.565{col 65}{space 4}-.0000721{col 78}{space 3} .0001319
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{txt}{space 15}age_tchr {c |}{col 25}{res}{space 2} .0108059{col 37}{space 2}   .01399{col 48}{space 1}    0.77{col 57}{space 3}0.440{col 65}{space 4} -.016614{col 78}{space 3} .0382258
{txt}{space 11}phone_survey {c |}{col 25}{res}{space 2} .2056078{col 37}{space 2} .1208237{col 48}{space 1}    1.70{col 57}{space 3}0.089{col 65}{space 4}-.0312023{col 78}{space 3} .4424179
{txt}{space 18}_cons {c |}{col 25}{res}{space 2} .3126272{col 37}{space 2} .7282489{col 48}{space 1}    0.43{col 57}{space 3}0.668{col 65}{space 4}-1.114714{col 78}{space 3} 1.739969
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 79}Note: One or more parameters could not be estimated in 32 bootstrap replicates; standard-error estimates include only complete replications.{p_end}

{com}. matrix did_cog = r(table)
{txt}
{com}. scalar did_cog_n = e(N) / 2
{txt}
{com}. restore
{txt}
{com}. 
. // CPCS
. preserve
{txt}
{com}. keep if CPCS_std != .
{txt}(6 observations deleted)

{com}. gen outcome_1 = CPCS_std 
{txt}
{com}. gen outcome_0 = cpcs_pre_std_missing_0 
{txt}
{com}. reshape long outcome_, i(student_no) j(time)
{txt}(j = 0 1)

Data{col 36}Wide{col 43}->{col 48}Long
{hline 77}
Number of observations     {res}         190   {txt}->   {res}380         
{txt}Number of variables        {res}       1,271   {txt}->   {res}1,271       
{txt}j variable (2 values)                     ->   {res}time
{txt}xij variables:
                    {res}outcome_0 outcome_1   {txt}->   {res}outcome_
{txt}{hline 77}

{com}. reg outcome_ i.treatment##i.time DT_score_pre_std_missing_0 rosen_pre_std_missing_0 `controls', vce(boot, cluster(school_no) reps(1000))
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{text}Bootstrap replications ({result:1,000}){text}: 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done
{res}{text}{text:{bf:x}}: Error occurred when {bf:bootstrap} executed {bf:regress}.
{res}
{txt}{col 1}Linear regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:380}
{txt}{col 57}{lalign 13:Replications}{col 70} = {res}{ralign 6:946}
{txt}{col 57}{lalign 13:Wald chi2({res:20})}{col 70} = {res}{ralign 6:335.10}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 57}{lalign 13:R-squared}{col 70} = {res}{ralign 6:0.3179}
{txt}{col 57}{lalign 13:Adj R-squared}{col 70} = {res}{ralign 6:0.2798}
{txt}{col 57}{lalign 13:Root MSE}{col 70} = {res}{ralign 6:0.8669}

{txt}{ralign 92:(Replications based on {res:32} clusters in {res:school_no})}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}   Observed{col 40}   Bootstrap{col 68}         Norm{col 81}al-based
{col 1}                  outcome_{col 28}{c |} coefficient{col 40}  std. err.{col 52}      z{col 60}   P>|z|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}1.treatment {c |}{col 28}{res}{space 2} .2355758{col 40}{space 2} .1367353{col 51}{space 1}    1.72{col 60}{space 3}0.085{col 68}{space 4}-.0324206{col 81}{space 3} .5035721
{txt}{space 20}1.time {c |}{col 28}{res}{space 2}-.0579986{col 40}{space 2} .1576984{col 51}{space 1}   -0.37{col 60}{space 3}0.713{col 68}{space 4}-.3670818{col 81}{space 3} .2510847
{txt}{space 26} {c |}
{space 12}treatment#time {c |}
{space 22}1 1  {c |}{col 28}{res}{space 2} .3273837{col 40}{space 2} .2941549{col 51}{space 1}    1.11{col 60}{space 3}0.266{col 68}{space 4}-.2491493{col 81}{space 3} .9039166
{txt}{space 26} {c |}
DT_score_pre_std_missing_0 {c |}{col 28}{res}{space 2} -.012118{col 40}{space 2} .0336049{col 51}{space 1}   -0.36{col 60}{space 3}0.718{col 68}{space 4}-.0779824{col 81}{space 3} .0537464
{txt}{space 3}rosen_pre_std_missing_0 {c |}{col 28}{res}{space 2} .4607542{col 40}{space 2}  .035974{col 51}{space 1}   12.81{col 60}{space 3}0.000{col 68}{space 4} .3902466{col 81}{space 3} .5312619
{txt}{space 19}4.grade {c |}{col 28}{res}{space 2}-.0841095{col 40}{space 2} .1385469{col 51}{space 1}   -0.61{col 60}{space 3}0.544{col 68}{space 4}-.3556565{col 81}{space 3} .1874375
{txt}{space 22}gend {c |}{col 28}{res}{space 2} .1014079{col 40}{space 2}   .08679{col 51}{space 1}    1.17{col 60}{space 3}0.243{col 68}{space 4}-.0686973{col 81}{space 3} .2715132
{txt}{space 19}branch1 {c |}{col 28}{res}{space 2} .0609343{col 40}{space 2}   .25979{col 51}{space 1}    0.23{col 60}{space 3}0.815{col 68}{space 4}-.4482448{col 81}{space 3} .5701134
{txt}{space 19}branch2 {c |}{col 28}{res}{space 2} .1337413{col 40}{space 2} .4314971{col 51}{space 1}    0.31{col 60}{space 3}0.757{col 68}{space 4}-.7119775{col 81}{space 3} .9794601
{txt}{space 19}branch3 {c |}{col 28}{res}{space 2}  .185824{col 40}{space 2}  .285701{col 51}{space 1}    0.65{col 60}{space 3}0.515{col 68}{space 4}-.3741396{col 81}{space 3} .7457877
{txt}{space 12}income_source1 {c |}{col 28}{res}{space 2} .2774561{col 40}{space 2} .4541467{col 51}{space 1}    0.61{col 60}{space 3}0.541{col 68}{space 4} -.612655{col 81}{space 3} 1.167567
{txt}{space 12}income_source2 {c |}{col 28}{res}{space 2} .6377754{col 40}{space 2} .3132143{col 51}{space 1}    2.04{col 60}{space 3}0.042{col 68}{space 4} .0238868{col 81}{space 3} 1.251664
{txt}{space 12}income_source3 {c |}{col 28}{res}{space 2} .5341619{col 40}{space 2} .3215834{col 51}{space 1}    1.66{col 60}{space 3}0.097{col 68}{space 4}-.0961299{col 81}{space 3} 1.164454
{txt}{space 12}income_source4 {c |}{col 28}{res}{space 2}-1.445426{col 40}{space 2} 1.003148{col 51}{space 1}   -1.44{col 60}{space 3}0.150{col 68}{space 4} -3.41156{col 81}{space 3} .5207084
{txt}{space 4}last_income_per_member {c |}{col 28}{res}{space 2}-.0000233{col 40}{space 2} .0000208{col 51}{space 1}   -1.12{col 60}{space 3}0.263{col 68}{space 4}-.0000641{col 81}{space 3} .0000175
{txt}{space 18}hhmember {c |}{col 28}{res}{space 2} .0266898{col 40}{space 2} .0381087{col 51}{space 1}    0.70{col 60}{space 3}0.484{col 68}{space 4} -.048002{col 81}{space 3} .1013816
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{txt}{space 13}hhheadeduyear {c |}{col 28}{res}{space 2}-.0037429{col 40}{space 2} .0112619{col 51}{space 1}   -0.33{col 60}{space 3}0.740{col 68}{space 4}-.0258159{col 81}{space 3}   .01833
{txt}{space 18}age_tchr {c |}{col 28}{res}{space 2}-.0014402{col 40}{space 2} .0164578{col 51}{space 1}   -0.09{col 60}{space 3}0.930{col 68}{space 4}-.0336968{col 81}{space 3} .0308164
{txt}{space 14}phone_survey {c |}{col 28}{res}{space 2}-.0713721{col 40}{space 2} .0919736{col 51}{space 1}   -0.78{col 60}{space 3}0.438{col 68}{space 4} -.251637{col 81}{space 3} .1088929
{txt}{space 21}_cons {c |}{col 28}{res}{space 2}-.7948112{col 40}{space 2}  .517355{col 51}{space 1}   -1.54{col 60}{space 3}0.124{col 68}{space 4}-1.808808{col 81}{space 3} .2191858
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 79}Note: One or more parameters could not be estimated in 54 bootstrap replicates; standard-error estimates include only complete replications.{p_end}

{com}. matrix did_cpcs = r(table)
{txt}
{com}. scalar did_cpcs_n = e(N) / 2
{txt}
{com}. restore
{txt}
{com}. 
. // RSES
. preserve
{txt}
{com}. keep if RSES_std != .
{txt}(6 observations deleted)

{com}. gen outcome_1 = RSES_std
{txt}
{com}. gen outcome_0 = rosen_pre_std_missing_0
{txt}
{com}. reshape long outcome_, i(student_no) j(time)
{txt}(j = 0 1)

Data{col 36}Wide{col 43}->{col 48}Long
{hline 77}
Number of observations     {res}         190   {txt}->   {res}380         
{txt}Number of variables        {res}       1,271   {txt}->   {res}1,271       
{txt}j variable (2 values)                     ->   {res}time
{txt}xij variables:
                    {res}outcome_0 outcome_1   {txt}->   {res}outcome_
{txt}{hline 77}

{com}. reg outcome_ i.treatment##i.time DT_score_pre_std_missing_0 cpcs_pre_std_missing_0 `controls', vce(boot, cluster(school_no) reps(1000))
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{text}Bootstrap replications ({result:1,000}){text}: 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done
{res}{text}{text:{bf:x}}: Error occurred when {bf:bootstrap} executed {bf:regress}.
{res}
{txt}{col 1}Linear regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:380}
{txt}{col 57}{lalign 13:Replications}{col 70} = {res}{ralign 6:936}
{txt}{col 57}{lalign 13:Wald chi2({res:20})}{col 70} = {res}{ralign 6:297.16}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 57}{lalign 13:R-squared}{col 70} = {res}{ralign 6:0.3045}
{txt}{col 57}{lalign 13:Adj R-squared}{col 70} = {res}{ralign 6:0.2658}
{txt}{col 57}{lalign 13:Root MSE}{col 70} = {res}{ralign 6:0.8804}

{txt}{ralign 92:(Replications based on {res:32} clusters in {res:school_no})}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}   Observed{col 40}   Bootstrap{col 68}         Norm{col 81}al-based
{col 1}                  outcome_{col 28}{c |} coefficient{col 40}  std. err.{col 52}      z{col 60}   P>|z|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}1.treatment {c |}{col 28}{res}{space 2}-.0993937{col 40}{space 2}  .143346{col 51}{space 1}   -0.69{col 60}{space 3}0.488{col 68}{space 4}-.3803467{col 81}{space 3} .1815593
{txt}{space 20}1.time {c |}{col 28}{res}{space 2}-.1787243{col 40}{space 2} .1840554{col 51}{space 1}   -0.97{col 60}{space 3}0.332{col 68}{space 4}-.5394663{col 81}{space 3} .1820177
{txt}{space 26} {c |}
{space 12}treatment#time {c |}
{space 22}1 1  {c |}{col 28}{res}{space 2} .5456764{col 40}{space 2} .3180818{col 51}{space 1}    1.72{col 60}{space 3}0.086{col 68}{space 4}-.0777525{col 81}{space 3} 1.169105
{txt}{space 26} {c |}
DT_score_pre_std_missing_0 {c |}{col 28}{res}{space 2}-.0969414{col 40}{space 2} .0352807{col 51}{space 1}   -2.75{col 60}{space 3}0.006{col 68}{space 4}-.1660904{col 81}{space 3}-.0277924
{txt}{space 4}cpcs_pre_std_missing_0 {c |}{col 28}{res}{space 2} .4461733{col 40}{space 2} .0373333{col 51}{space 1}   11.95{col 60}{space 3}0.000{col 68}{space 4} .3730014{col 81}{space 3} .5193452
{txt}{space 19}4.grade {c |}{col 28}{res}{space 2}-.1083238{col 40}{space 2} .1163355{col 51}{space 1}   -0.93{col 60}{space 3}0.352{col 68}{space 4}-.3363371{col 81}{space 3} .1196896
{txt}{space 22}gend {c |}{col 28}{res}{space 2} .1021485{col 40}{space 2} .0927716{col 51}{space 1}    1.10{col 60}{space 3}0.271{col 68}{space 4}-.0796806{col 81}{space 3} .2839775
{txt}{space 19}branch1 {c |}{col 28}{res}{space 2} .0415224{col 40}{space 2}  .240617{col 51}{space 1}    0.17{col 60}{space 3}0.863{col 68}{space 4}-.4300784{col 81}{space 3} .5131231
{txt}{space 19}branch2 {c |}{col 28}{res}{space 2} .2980852{col 40}{space 2} .3144157{col 51}{space 1}    0.95{col 60}{space 3}0.343{col 68}{space 4}-.3181581{col 81}{space 3} .9143286
{txt}{space 19}branch3 {c |}{col 28}{res}{space 2} .4021137{col 40}{space 2} .2333113{col 51}{space 1}    1.72{col 60}{space 3}0.085{col 68}{space 4}-.0551681{col 81}{space 3} .8593955
{txt}{space 12}income_source1 {c |}{col 28}{res}{space 2} .2334346{col 40}{space 2}  .489668{col 51}{space 1}    0.48{col 60}{space 3}0.634{col 68}{space 4} -.726297{col 81}{space 3} 1.193166
{txt}{space 12}income_source2 {c |}{col 28}{res}{space 2} .5325627{col 40}{space 2} .3718377{col 51}{space 1}    1.43{col 60}{space 3}0.152{col 68}{space 4}-.1962258{col 81}{space 3} 1.261351
{txt}{space 12}income_source3 {c |}{col 28}{res}{space 2} .4627215{col 40}{space 2} .3778129{col 51}{space 1}    1.22{col 60}{space 3}0.221{col 68}{space 4}-.2777782{col 81}{space 3} 1.203221
{txt}{space 12}income_source4 {c |}{col 28}{res}{space 2}  -1.2251{col 40}{space 2} 1.173635{col 51}{space 1}   -1.04{col 60}{space 3}0.297{col 68}{space 4}-3.525383{col 81}{space 3} 1.075182
{txt}{space 4}last_income_per_member {c |}{col 28}{res}{space 2}-.0000245{col 40}{space 2} .0000363{col 51}{space 1}   -0.68{col 60}{space 3}0.499{col 68}{space 4}-.0000956{col 81}{space 3} .0000466
{txt}{space 18}hhmember {c |}{col 28}{res}{space 2} .0016118{col 40}{space 2} .0378357{col 51}{space 1}    0.04{col 60}{space 3}0.966{col 68}{space 4}-.0725449{col 81}{space 3} .0757685
{txt}{space 17}hhheadage {c |}{col 28}{res}{space 2} -.001226{col 40}{space 2} .0060048{col 51}{space 1}   -0.20{col 60}{space 3}0.838{col 68}{space 4}-.0129951{col 81}{space 3} .0105432
{txt}{space 13}hhheadeduyear {c |}{col 28}{res}{space 2}-.0131627{col 40}{space 2} .0131828{col 51}{space 1}   -1.00{col 60}{space 3}0.318{col 68}{space 4}-.0390006{col 81}{space 3} .0126752
{txt}{space 18}age_tchr {c |}{col 28}{res}{space 2}-.0035833{col 40}{space 2} .0121757{col 51}{space 1}   -0.29{col 60}{space 3}0.769{col 68}{space 4}-.0274471{col 81}{space 3} .0202806
{txt}{space 14}phone_survey {c |}{col 28}{res}{space 2}-.0471846{col 40}{space 2}  .096836{col 51}{space 1}   -0.49{col 60}{space 3}0.626{col 68}{space 4}-.2369797{col 81}{space 3} .1426106
{txt}{space 21}_cons {c |}{col 28}{res}{space 2}-.4814518{col 40}{space 2}  .532291{col 51}{space 1}   -0.90{col 60}{space 3}0.366{col 68}{space 4}-1.524723{col 81}{space 3} .5618195
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 79}Note: One or more parameters could not be estimated in 64 bootstrap replicates; standard-error estimates include only complete replications.{p_end}

{com}. matrix did_rses = r(table)
{txt}
{com}. scalar did_rses_n = e(N) / 2
{txt}
{com}. restore
{txt}
{com}. 
. 
. // significant level
. 
. local outcome cog rses cpcs
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. forvalues i = 1/9 {c -(}
{txt}  3{com}.                 if did_`dep'[4,`i']<=0.01 {c -(}
{txt}  4{com}.                         local star_`dep'_`i' %3s "***"
{txt}  5{com}.                 {c )-}
{txt}  6{com}.                 else if (did_`dep'[4,`i']>0.01) & (did_`dep'[4,`i']<=0.05) {c -(}
{txt}  7{com}.                         local star_`dep'_`i' %2s "**"
{txt}  8{com}.                 {c )-}
{txt}  9{com}.                 else if (did_`dep'[4,`i']>0.05) & (did_`dep'[4,`i']<=0.10) {c -(}
{txt} 10{com}.                         local star_`dep'_`i' %1s "*"
{txt} 11{com}.                 {c )-}
{txt} 12{com}.                 else {c -(}
{txt} 13{com}.                         local star_`dep'_`i'  ""
{txt} 14{com}.                 {c )-}
{txt} 15{com}. {c )-}
{txt} 16{com}. {c )-} 
{txt}
{com}. 
. /// Table
> tempname hh2
{txt}
{com}. file open `hh2' using "$path_output/did_matchsample.tex", write replace
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "% Author: Kazuma Takakura" _newline
{txt}
{com}. file write `hh2' "% Date: `c(current_date)'" _newline
{txt}
{com}. file write `hh2' "% Time: `c(current_time)'" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. 
. file write `hh2' "\begin{c -(}table{c )-}[h!]\footnotesize" _newline
{txt}
{com}. file write `hh2' "  \centering" _newline
{txt}
{com}. file write `hh2' "  \caption{c -(}Difference in Differences (Matched Sample){c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:did_psm{c )-}" _newline
{txt}
{com}. file write `hh2' "\scalebox{c -(}1{c )-}{c -(}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}threeparttable{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "\begin{c -(}tabular{c )-}{c -(}lccc{c )-}\toprule" _newline
{txt}
{com}. 
.   
. file write `hh2' "  & Rapid math test score &  RSES score  & CPCS score   \\\midrule\midrule" _newline
{txt}
{com}. 
. file write `hh2' "  DiD & " %04.3f (did_cog[1,8]) `star_cog_8' "  & " %04.3f (did_rses[1,8]) `star_rses_8' " & " %04.3f (did_cpcs[1,8]) `star_cpcs_8' " \\ " _newline
{txt}
{com}. file write `hh2' "    & (" %04.3f (did_cog[2,8]) ") & (" %04.3f (did_rses[2,8]) ") & (" %04.3f (did_cpcs[2,8]) ") \\ " _newline
{txt}
{com}. 
. file write `hh2' "  Treatment & " %04.3f (did_cog[1,2]) `star_cog_2' "  & " %04.3f (did_rses[1,2]) `star_rses_2' " & " %04.3f (did_cpcs[1,2]) `star_cpcs_2' " \\ " _newline
{txt}
{com}. file write `hh2' "    & (" %04.3f (did_cog[2,2]) ") & (" %04.3f (did_rses[2,2]) ") & (" %04.3f (did_cpcs[2,2]) ") \\ " _newline
{txt}
{com}. 
. file write `hh2' "  After & " %04.3f (did_cog[1,4]) `star_cog_4' "  & " %04.3f (did_rses[1,4]) `star_rses_4' " & " %04.3f (did_cpcs[1,4]) `star_cpcs_4' " \\ " _newline
{txt}
{com}. file write `hh2' "    & (" %04.3f (did_cog[2,4]) ") & (" %04.3f (did_rses[2,4]) ") & (" %04.3f (did_cpcs[2,4]) ") \\ " _newline
{txt}
{com}. 
. file write `hh2' "  Constant & " %04.3f (did_cog[1,26]) `star_cog_26' "  & " %04.3f (did_rses[1,26]) `star_rses_26' " & " %04.3f (did_cpcs[1,26]) `star_cpcs_26' " \\ " _newline
{txt}
{com}. file write `hh2' "    & (" %04.3f (did_cog[2,26]) ") & (" %04.3f (did_rses[2,26]) ") & (" %04.3f (did_cpcs[2,26]) ") \\ " _newline
{txt}
{com}. 
. file write `hh2' "  Observations & " (did_cog_n) "  & " (did_rses_n) " & " (did_cpcs_n) " \\ " _newline
{txt}
{com}. 
. file write `hh2' "\midrule" _newline
{txt}
{com}. file write `hh2' "\end{c -(}tabular{c )-}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\item (a) We control student's grade, sex, baseline cognitive and baseline non-cognitive score, DT baseline time, branch dummy (location), parents' income source, last income per family member, number of household members, age of household head, education level of household head, teacher's age, sex, and phone survey dummy. The same variables are used for propensity score matching." _newline
{txt}
{com}. file write `hh2' "\item (b) School-clustered bootstrap standard errors are reported within parentheses. " _newline
{txt}
{com}. file write `hh2' "\item (c) $^*$ Significant at 10\% level; $^{c -(}**{c )-}$ significant at 5\% level; $^{c -(}***{c )-}$ significant at 1\% level. " _newline
{txt}
{com}. file write `hh2' "\end{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}threeparttable{c )-}" _newline
{txt}
{com}. file write `hh2' "{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:addlabel{c )-}%" _newline
{txt}
{com}. file write `hh2' "\end{c -(}table{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. file close `hh2'
{txt}
{com}. 
{txt}end of do-file

{com}. 
. do "$path_do/3_table_F4.do"
{txt}
{com}. * This is the do file to create "Table F4. Inverse Probability Weighted Difference in Differences (Matched Sample)"
. set seed 123
{txt}
{com}. 
. use "$path_data/temp/followup_student_parents_matched", clear
{txt}
{com}. 
. gen gend = q1d - 1
{txt}
{com}. 
. local controls i.grade gend branch1 branch2 branch3 income_source1 income_source2 income_source3 income_source4 last_income_per_member hhmember hhheadage hhheadeduyear age_tchr phone_survey
{txt}
{com}. teffects psmatch (followup_cog_std) (treatment DT_score_pre_std_missing_0 cpcs_pre_std_missing_0 rosen_pre_std_missing_0 `controls') 
{res}
{txt}Treatment-effects estimation{col 48}Number of obs {col 67}= {res}       243
{txt:Estimator}{col 16}:{res: propensity-score matching}{col 48}{txt:Matches: requested }{col 67}{txt:=}          1
{txt:Outcome model}{col 16}:{res: matching}{txt}{col 63}min {col 67}= {res}         1
{txt:Treatment model}{col 16}:{res: logit}{col 63}{txt:max }{col 67}{txt:=}          1
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}   AI robust
{col 1}followup_c~d{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ATE          {txt}{c |}
{space 3}treatment {c |}
{space 3}(1 vs 0)  {c |}{col 14}{res}{space 2}-.2234479{col 26}{space 2} .1415597{col 37}{space 1}   -1.58{col 46}{space 3}0.114{col 54}{space 4}-.5008998{col 67}{space 3} .0540039
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. psmatch2 treatment `controls', outcome(followup_cog_std) noreplacement
{res}
{txt}{col 1}Probit regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:243}
{txt}{col 57}{lalign 13:LR chi2({res:15})}{col 70} = {res}{ralign 6:34.58}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0028}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 9:-146.5686}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.1055}

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}             treatment{col 24}{c |} Coefficient{col 36}  Std. err.{col 48}      z{col 56}   P>|z|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}4.grade {c |}{col 24}{res}{space 2}    .1615{col 36}{space 2}  .199106{col 47}{space 1}    0.81{col 56}{space 3}0.417{col 64}{space 4}-.2287407{col 77}{space 3} .5517406
{txt}{space 18}gend {c |}{col 24}{res}{space 2} .0762294{col 36}{space 2} .1805964{col 47}{space 1}    0.42{col 56}{space 3}0.673{col 64}{space 4} -.277733{col 77}{space 3} .4301919
{txt}{space 15}branch1 {c |}{col 24}{res}{space 2}-.6131427{col 36}{space 2} .2913104{col 47}{space 1}   -2.10{col 56}{space 3}0.035{col 64}{space 4}-1.184101{col 77}{space 3}-.0421848
{txt}{space 15}branch2 {c |}{col 24}{res}{space 2} .0633647{col 36}{space 2} .3409269{col 47}{space 1}    0.19{col 56}{space 3}0.853{col 64}{space 4}-.6048398{col 77}{space 3} .7315691
{txt}{space 15}branch3 {c |}{col 24}{res}{space 2}-.7760038{col 36}{space 2} .2437862{col 47}{space 1}   -3.18{col 56}{space 3}0.001{col 64}{space 4}-1.253816{col 77}{space 3}-.2981916
{txt}{space 8}income_source1 {c |}{col 24}{res}{space 2}-.8678153{col 36}{space 2} .7131824{col 47}{space 1}   -1.22{col 56}{space 3}0.224{col 64}{space 4}-2.265627{col 77}{space 3} .5299964
{txt}{space 8}income_source2 {c |}{col 24}{res}{space 2}-.0705556{col 36}{space 2} .5618473{col 47}{space 1}   -0.13{col 56}{space 3}0.900{col 64}{space 4}-1.171756{col 77}{space 3} 1.030645
{txt}{space 8}income_source3 {c |}{col 24}{res}{space 2}-.2605725{col 36}{space 2} .5411181{col 47}{space 1}   -0.48{col 56}{space 3}0.630{col 64}{space 4}-1.321145{col 77}{space 3} .7999995
{txt}{space 8}income_source4 {c |}{col 24}{res}{space 2} 1.193503{col 36}{space 2} 1.660674{col 47}{space 1}    0.72{col 56}{space 3}0.472{col 64}{space 4}-2.061357{col 77}{space 3} 4.448364
{txt}last_income_per_member {c |}{col 24}{res}{space 2}-.0001101{col 36}{space 2} .0000925{col 47}{space 1}   -1.19{col 56}{space 3}0.234{col 64}{space 4}-.0002915{col 77}{space 3} .0000712
{txt}{space 14}hhmember {c |}{col 24}{res}{space 2} .1039315{col 36}{space 2} .0728872{col 47}{space 1}    1.43{col 56}{space 3}0.154{col 64}{space 4}-.0389248{col 77}{space 3} .2467879
{txt}{space 13}hhheadage {c |}{col 24}{res}{space 2}-.0046784{col 36}{space 2} .0099531{col 47}{space 1}   -0.47{col 56}{space 3}0.638{col 64}{space 4}-.0241861{col 77}{space 3} .0148292
{txt}{space 9}hhheadeduyear {c |}{col 24}{res}{space 2} -.046044{col 36}{space 2} .0274486{col 47}{space 1}   -1.68{col 56}{space 3}0.093{col 64}{space 4}-.0998422{col 77}{space 3} .0077542
{txt}{space 14}age_tchr {c |}{col 24}{res}{space 2}-.0242375{col 36}{space 2} .0143822{col 47}{space 1}   -1.69{col 56}{space 3}0.092{col 64}{space 4}-.0524262{col 77}{space 3} .0039511
{txt}{space 10}phone_survey {c |}{col 24}{res}{space 2}-.0365591{col 36}{space 2} .2042384{col 47}{space 1}   -0.18{col 56}{space 3}0.858{col 64}{space 4}-.4368591{col 77}{space 3} .3637408
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} 1.423968{col 36}{space 2} .8425599{col 47}{space 1}    1.69{col 56}{space 3}0.091{col 64}{space 4}-.2274193{col 77}{space 3} 3.075355
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}{hline 28}{c TT}{hline 59}
        Variable     Sample {c |}    Treated     Controls   Difference         S.E.   T-stat
{hline 28}{c +}{hline 59}
followup_cog_std  Unmatched {c |}{res}-.092040852   .136183089  -.228223941   .130213149    -1.75
{txt}{col 17}        ATT {c |}{res}-.209204736   .136183089  -.345387825   .139668944    -2.47
{txt}{hline 28}{c +}{hline 59}
Note: S.E. does not take into account that the propensity score is estimated.

 psmatch2: {c |}   psmatch2: Common
 Treatment {c |}        support
assignment {c |} Off suppo  On suppor {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
 Untreated {c |}{res}         0         98 {txt}{c |}{res}        98 
{txt}   Treated {c |}{res}        47         98 {txt}{c |}{res}       145 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}        47        196 {txt}{c |}{res}       243 
{txt}
{com}. gen psmattrition = 1 if _support!=1
{txt}(196 missing values generated)

{com}. recode psmattrition (.=0)
{txt}(196 changes made to {bf:psmattrition})

{com}. keep if psmattrition == 0
{txt}(47 observations deleted)

{com}. 
. logit treatment DT_score_pre_std_missing_0 cpcs_pre_std_missing_0 rosen_pre_std_missing_0 `controls'

{res}{txt}Iteration 0:{space 2}Log likelihood = {res:-135.85685}  
Iteration 1:{space 2}Log likelihood = {res:-123.82096}  
Iteration 2:{space 2}Log likelihood = {res:  -123.804}  
Iteration 3:{space 2}Log likelihood = {res:-123.80396}  
Iteration 4:{space 2}Log likelihood = {res:-123.80396}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:196}
{txt}{col 57}{lalign 13:LR chi2({res:18})}{col 70} = {res}{ralign 6:24.11}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.1516}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-123.80396}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0887}

{txt}{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                 treatment{col 28}{c |} Coefficient{col 40}  Std. err.{col 52}      z{col 60}   P>|z|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
DT_score_pre_std_missing_0 {c |}{col 28}{res}{space 2}-.2889574{col 40}{space 2} .1661152{col 51}{space 1}   -1.74{col 60}{space 3}0.082{col 68}{space 4}-.6145372{col 81}{space 3} .0366225
{txt}{space 4}cpcs_pre_std_missing_0 {c |}{col 28}{res}{space 2} 1.595615{col 40}{space 2} .4156516{col 51}{space 1}    3.84{col 60}{space 3}0.000{col 68}{space 4} .7809529{col 81}{space 3} 2.410277
{txt}{space 3}rosen_pre_std_missing_0 {c |}{col 28}{res}{space 2}  -1.3837{col 40}{space 2} .4080736{col 51}{space 1}   -3.39{col 60}{space 3}0.001{col 68}{space 4} -2.18351{col 81}{space 3}-.5838908
{txt}{space 19}4.grade {c |}{col 28}{res}{space 2} -.331723{col 40}{space 2} .3789491{col 51}{space 1}   -0.88{col 60}{space 3}0.381{col 68}{space 4}-1.074449{col 81}{space 3} .4110035
{txt}{space 22}gend {c |}{col 28}{res}{space 2}-.0489827{col 40}{space 2} .3287012{col 51}{space 1}   -0.15{col 60}{space 3}0.882{col 68}{space 4}-.6932253{col 81}{space 3} .5952599
{txt}{space 19}branch1 {c |}{col 28}{res}{space 2} .2852899{col 40}{space 2} .5997352{col 51}{space 1}    0.48{col 60}{space 3}0.634{col 68}{space 4}-.8901695{col 81}{space 3} 1.460749
{txt}{space 19}branch2 {c |}{col 28}{res}{space 2} .5138213{col 40}{space 2} .6645993{col 51}{space 1}    0.77{col 60}{space 3}0.439{col 68}{space 4}-.7887693{col 81}{space 3} 1.816412
{txt}{space 19}branch3 {c |}{col 28}{res}{space 2} .3449286{col 40}{space 2} .5234126{col 51}{space 1}    0.66{col 60}{space 3}0.510{col 68}{space 4}-.6809413{col 81}{space 3} 1.370799
{txt}{space 12}income_source1 {c |}{col 28}{res}{space 2}-.5807246{col 40}{space 2}  1.30254{col 51}{space 1}   -0.45{col 60}{space 3}0.656{col 68}{space 4}-3.133656{col 81}{space 3} 1.972207
{txt}{space 12}income_source2 {c |}{col 28}{res}{space 2} .1564282{col 40}{space 2} 1.109698{col 51}{space 1}    0.14{col 60}{space 3}0.888{col 68}{space 4}-2.018539{col 81}{space 3} 2.331396
{txt}{space 12}income_source3 {c |}{col 28}{res}{space 2}-.0120761{col 40}{space 2} 1.068239{col 51}{space 1}   -0.01{col 60}{space 3}0.991{col 68}{space 4}-2.105786{col 81}{space 3} 2.081634
{txt}{space 12}income_source4 {c |}{col 28}{res}{space 2} .4354398{col 40}{space 2} 3.248715{col 51}{space 1}    0.13{col 60}{space 3}0.893{col 68}{space 4}-5.931925{col 81}{space 3} 6.802805
{txt}{space 4}last_income_per_member {c |}{col 28}{res}{space 2}-.0000662{col 40}{space 2} .0001039{col 51}{space 1}   -0.64{col 60}{space 3}0.524{col 68}{space 4}-.0002698{col 81}{space 3} .0001375
{txt}{space 18}hhmember {c |}{col 28}{res}{space 2} .0258684{col 40}{space 2} .1369669{col 51}{space 1}    0.19{col 60}{space 3}0.850{col 68}{space 4}-.2425817{col 81}{space 3} .2943185
{txt}{space 17}hhheadage {c |}{col 28}{res}{space 2}   -.0033{col 40}{space 2} .0176977{col 51}{space 1}   -0.19{col 60}{space 3}0.852{col 68}{space 4}-.0379868{col 81}{space 3} .0313869
{txt}{space 13}hhheadeduyear {c |}{col 28}{res}{space 2}-.0405108{col 40}{space 2} .0472395{col 51}{space 1}   -0.86{col 60}{space 3}0.391{col 68}{space 4}-.1330985{col 81}{space 3}  .052077
{txt}{space 18}age_tchr {c |}{col 28}{res}{space 2} .0183082{col 40}{space 2} .0294472{col 51}{space 1}    0.62{col 60}{space 3}0.534{col 68}{space 4}-.0394073{col 81}{space 3} .0760237
{txt}{space 14}phone_survey {c |}{col 28}{res}{space 2}-.0536221{col 40}{space 2} .3573724{col 51}{space 1}   -0.15{col 60}{space 3}0.881{col 68}{space 4}-.7540592{col 81}{space 3}  .646815
{txt}{space 21}_cons {c |}{col 28}{res}{space 2}-.3591307{col 40}{space 2} 1.696132{col 51}{space 1}   -0.21{col 60}{space 3}0.832{col 68}{space 4}-3.683489{col 81}{space 3} 2.965228
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. predict pscore
{txt}(option {bf:pr} assumed; Pr(treatment))

{com}. gen weight = treatment/pscore + (1-treatment)/(1-pscore)
{txt}
{com}. 
. // Cog
. preserve
{txt}
{com}. gen outcome_1 = followup_cog_std 
{txt}
{com}. gen outcome_0 = DT_score_pre_std_missing_0
{txt}
{com}. reshape long outcome_, i(student_no) j(time)
{txt}(j = 0 1)

Data{col 36}Wide{col 43}->{col 48}Long
{hline 77}
Number of observations     {res}         196   {txt}->   {res}392         
{txt}Number of variables        {res}       1,273   {txt}->   {res}1,273       
{txt}j variable (2 values)                     ->   {res}time
{txt}xij variables:
                    {res}outcome_0 outcome_1   {txt}->   {res}outcome_
{txt}{hline 77}

{com}. reg outcome_ i.treatment##i.time cpcs_pre_std_missing_0 rosen_pre_std_missing_0 `controls' [pweight=weight], cluster(school_no)
{txt}(sum of wgt is 789.7644240856171)

Linear regression                               Number of obs     = {res}       392
                                                {txt}F(20, 31)         =  {res}     2.77
                                                {txt}Prob > F          = {res}    0.0053
                                                {txt}R-squared         = {res}    0.0830
                                                {txt}Root MSE          =    {res} .96835

{txt}{ralign 89:(Std. err. adjusted for {res:32} clusters in {res:school_no})}
{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}               outcome_{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      t{col 57}   P>|t|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}1.treatment {c |}{col 25}{res}{space 2} .0431928{col 37}{space 2} .1992489{col 48}{space 1}    0.22{col 57}{space 3}0.830{col 65}{space 4}-.3631781{col 78}{space 3} .4495636
{txt}{space 17}1.time {c |}{col 25}{res}{space 2} .2102908{col 37}{space 2} .2063619{col 48}{space 1}    1.02{col 57}{space 3}0.316{col 65}{space 4}-.2105871{col 78}{space 3} .6311686
{txt}{space 23} {c |}
{space 9}treatment#time {c |}
{space 19}1 1  {c |}{col 25}{res}{space 2}-.4217765{col 37}{space 2} .2529623{col 48}{space 1}   -1.67{col 57}{space 3}0.106{col 65}{space 4}-.9376964{col 78}{space 3} .0941434
{txt}{space 23} {c |}
{space 1}cpcs_pre_std_missing_0 {c |}{col 25}{res}{space 2} .1883177{col 37}{space 2} .1146673{col 48}{space 1}    1.64{col 57}{space 3}0.111{col 65}{space 4}-.0455479{col 78}{space 3} .4221833
{txt}rosen_pre_std_missing_0 {c |}{col 25}{res}{space 2}-.2188489{col 37}{space 2} .1274435{col 48}{space 1}   -1.72{col 57}{space 3}0.096{col 65}{space 4}-.4787717{col 78}{space 3} .0410739
{txt}{space 16}4.grade {c |}{col 25}{res}{space 2}-.1859579{col 37}{space 2}  .135991{col 48}{space 1}   -1.37{col 57}{space 3}0.181{col 65}{space 4}-.4633135{col 78}{space 3} .0913976
{txt}{space 19}gend {c |}{col 25}{res}{space 2}-.0648787{col 37}{space 2} .1084317{col 48}{space 1}   -0.60{col 57}{space 3}0.554{col 65}{space 4}-.2860266{col 78}{space 3} .1562693
{txt}{space 16}branch1 {c |}{col 25}{res}{space 2} .2028898{col 37}{space 2} .1992845{col 48}{space 1}    1.02{col 57}{space 3}0.317{col 65}{space 4}-.2035536{col 78}{space 3} .6093333
{txt}{space 16}branch2 {c |}{col 25}{res}{space 2} .0984517{col 37}{space 2}  .155461{col 48}{space 1}    0.63{col 57}{space 3}0.531{col 65}{space 4}-.2186132{col 78}{space 3} .4155166
{txt}{space 16}branch3 {c |}{col 25}{res}{space 2}-.0034641{col 37}{space 2} .1368173{col 48}{space 1}   -0.03{col 57}{space 3}0.980{col 65}{space 4}-.2825047{col 78}{space 3} .2755766
{txt}{space 9}income_source1 {c |}{col 25}{res}{space 2} .0348927{col 37}{space 2}  .428647{col 48}{space 1}    0.08{col 57}{space 3}0.936{col 65}{space 4}-.8393386{col 78}{space 3}  .909124
{txt}{space 9}income_source2 {c |}{col 25}{res}{space 2}-.1138275{col 37}{space 2} .3692432{col 48}{space 1}   -0.31{col 57}{space 3}0.760{col 65}{space 4} -.866904{col 78}{space 3}  .639249
{txt}{space 9}income_source3 {c |}{col 25}{res}{space 2}-.0528178{col 37}{space 2} .3251124{col 48}{space 1}   -0.16{col 57}{space 3}0.872{col 65}{space 4} -.715889{col 78}{space 3} .6102533
{txt}{space 9}income_source4 {c |}{col 25}{res}{space 2} .1311388{col 37}{space 2} 1.077569{col 48}{space 1}    0.12{col 57}{space 3}0.904{col 65}{space 4}-2.066578{col 78}{space 3} 2.328855
{txt}{space 1}last_income_per_member {c |}{col 25}{res}{space 2} .0000434{col 37}{space 2} .0000331{col 48}{space 1}    1.31{col 57}{space 3}0.199{col 65}{space 4}-.0000241{col 78}{space 3} .0001108
{txt}{space 15}hhmember {c |}{col 25}{res}{space 2} -.076333{col 37}{space 2} .0383421{col 48}{space 1}   -1.99{col 57}{space 3}0.055{col 65}{space 4}-.1545323{col 78}{space 3} .0018663
{txt}{space 14}hhheadage {c |}{col 25}{res}{space 2} -.004829{col 37}{space 2} .0061927{col 48}{space 1}   -0.78{col 57}{space 3}0.441{col 65}{space 4} -.017459{col 78}{space 3} .0078011
{txt}{space 10}hhheadeduyear {c |}{col 25}{res}{space 2} .0022666{col 37}{space 2} .0133759{col 48}{space 1}    0.17{col 57}{space 3}0.867{col 65}{space 4}-.0250139{col 78}{space 3}  .029547
{txt}{space 15}age_tchr {c |}{col 25}{res}{space 2} .0061053{col 37}{space 2}  .010013{col 48}{space 1}    0.61{col 57}{space 3}0.546{col 65}{space 4}-.0143163{col 78}{space 3} .0265269
{txt}{space 11}phone_survey {c |}{col 25}{res}{space 2} .2742642{col 37}{space 2} .1399381{col 48}{space 1}    1.96{col 57}{space 3}0.059{col 65}{space 4}-.0111415{col 78}{space 3} .5596699
{txt}{space 18}_cons {c |}{col 25}{res}{space 2} .3750232{col 37}{space 2} .6646335{col 48}{space 1}    0.56{col 57}{space 3}0.577{col 65}{space 4}-.9805058{col 78}{space 3} 1.730552
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. matrix did_cog = r(table)
{txt}
{com}. scalar did_cog_n = e(N) / 2
{txt}
{com}. restore
{txt}
{com}. 
. // CPCS
. preserve
{txt}
{com}. keep if CPCS_std != .
{txt}(6 observations deleted)

{com}. gen outcome_1 = CPCS_std 
{txt}
{com}. gen outcome_0 = cpcs_pre_std_missing_0 
{txt}
{com}. reshape long outcome_, i(student_no) j(time)
{txt}(j = 0 1)

Data{col 36}Wide{col 43}->{col 48}Long
{hline 77}
Number of observations     {res}         190   {txt}->   {res}380         
{txt}Number of variables        {res}       1,273   {txt}->   {res}1,273       
{txt}j variable (2 values)                     ->   {res}time
{txt}xij variables:
                    {res}outcome_0 outcome_1   {txt}->   {res}outcome_
{txt}{hline 77}

{com}. reg outcome_ i.treatment##i.time DT_score_pre_std_missing_0 rosen_pre_std_missing_0 `controls' [pweight=weight], cluster(school_no)
{txt}(sum of wgt is 763.0556974411011)

Linear regression                               Number of obs     = {res}       380
                                                {txt}F(20, 31)         =  {res}    81.09
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.3486
                                                {txt}Root MSE          =    {res} .87858

{txt}{ralign 92:(Std. err. adjusted for {res:32} clusters in {res:school_no})}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}                  outcome_{col 28}{c |} Coefficient{col 40}  std. err.{col 52}      t{col 60}   P>|t|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}1.treatment {c |}{col 28}{res}{space 2}-.0488925{col 40}{space 2} .1475473{col 51}{space 1}   -0.33{col 60}{space 3}0.743{col 68}{space 4}-.3498172{col 81}{space 3} .2520322
{txt}{space 20}1.time {c |}{col 28}{res}{space 2}-.2336289{col 40}{space 2}  .162926{col 51}{space 1}   -1.43{col 60}{space 3}0.162{col 68}{space 4}-.5659188{col 81}{space 3} .0986609
{txt}{space 26} {c |}
{space 12}treatment#time {c |}
{space 22}1 1  {c |}{col 28}{res}{space 2} .6813049{col 40}{space 2} .3251371{col 51}{space 1}    2.10{col 60}{space 3}0.044{col 68}{space 4} .0181835{col 81}{space 3} 1.344426
{txt}{space 26} {c |}
DT_score_pre_std_missing_0 {c |}{col 28}{res}{space 2}-.0168855{col 40}{space 2} .0321817{col 51}{space 1}   -0.52{col 60}{space 3}0.604{col 68}{space 4}-.0825205{col 81}{space 3} .0487496
{txt}{space 3}rosen_pre_std_missing_0 {c |}{col 28}{res}{space 2} .4789855{col 40}{space 2} .0277513{col 51}{space 1}   17.26{col 60}{space 3}0.000{col 68}{space 4} .4223863{col 81}{space 3} .5355846
{txt}{space 19}4.grade {c |}{col 28}{res}{space 2}-.1318919{col 40}{space 2} .1009998{col 51}{space 1}   -1.31{col 60}{space 3}0.201{col 68}{space 4}-.3378823{col 81}{space 3} .0740986
{txt}{space 22}gend {c |}{col 28}{res}{space 2} .1013662{col 40}{space 2} .0928719{col 51}{space 1}    1.09{col 60}{space 3}0.283{col 68}{space 4}-.0880473{col 81}{space 3} .2907797
{txt}{space 19}branch1 {c |}{col 28}{res}{space 2} .0507745{col 40}{space 2}  .183064{col 51}{space 1}    0.28{col 60}{space 3}0.783{col 68}{space 4}-.3225871{col 81}{space 3}  .424136
{txt}{space 19}branch2 {c |}{col 28}{res}{space 2} .0329201{col 40}{space 2} .2945771{col 51}{space 1}    0.11{col 60}{space 3}0.912{col 68}{space 4}-.5678739{col 81}{space 3} .6337142
{txt}{space 19}branch3 {c |}{col 28}{res}{space 2} .2388139{col 40}{space 2} .2090568{col 51}{space 1}    1.14{col 60}{space 3}0.262{col 68}{space 4}-.1875604{col 81}{space 3} .6651881
{txt}{space 12}income_source1 {c |}{col 28}{res}{space 2} .3520408{col 40}{space 2}  .427348{col 51}{space 1}    0.82{col 60}{space 3}0.416{col 68}{space 4}-.5195411{col 81}{space 3} 1.223623
{txt}{space 12}income_source2 {c |}{col 28}{res}{space 2} .6284983{col 40}{space 2} .2584527{col 51}{space 1}    2.43{col 60}{space 3}0.021{col 68}{space 4} .1013805{col 81}{space 3} 1.155616
{txt}{space 12}income_source3 {c |}{col 28}{res}{space 2} .4900778{col 40}{space 2} .2701374{col 51}{space 1}    1.81{col 60}{space 3}0.079{col 68}{space 4}-.0608711{col 81}{space 3} 1.041027
{txt}{space 12}income_source4 {c |}{col 28}{res}{space 2}-1.467541{col 40}{space 2} .8744761{col 51}{space 1}   -1.68{col 60}{space 3}0.103{col 68}{space 4}-3.251047{col 81}{space 3} .3159648
{txt}{space 4}last_income_per_member {c |}{col 28}{res}{space 2} -.000026{col 40}{space 2} 9.90e-06{col 51}{space 1}   -2.63{col 60}{space 3}0.013{col 68}{space 4}-.0000462{col 81}{space 3}-5.80e-06
{txt}{space 18}hhmember {c |}{col 28}{res}{space 2} .0160621{col 40}{space 2} .0402539{col 51}{space 1}    0.40{col 60}{space 3}0.693{col 68}{space 4}-.0660363{col 81}{space 3} .0981605
{txt}{space 17}hhheadage {c |}{col 28}{res}{space 2}-.0008983{col 40}{space 2} .0054155{col 51}{space 1}   -0.17{col 60}{space 3}0.869{col 68}{space 4}-.0119432{col 81}{space 3} .0101466
{txt}{space 13}hhheadeduyear {c |}{col 28}{res}{space 2} .0024551{col 40}{space 2} .0115015{col 51}{space 1}    0.21{col 60}{space 3}0.832{col 68}{space 4}-.0210025{col 81}{space 3} .0259126
{txt}{space 18}age_tchr {c |}{col 28}{res}{space 2} .0001379{col 40}{space 2} .0105959{col 51}{space 1}    0.01{col 60}{space 3}0.990{col 68}{space 4}-.0214726{col 81}{space 3} .0217484
{txt}{space 14}phone_survey {c |}{col 28}{res}{space 2}-.0922718{col 40}{space 2} .0856729{col 51}{space 1}   -1.08{col 60}{space 3}0.290{col 68}{space 4}-.2670027{col 81}{space 3} .0824592
{txt}{space 21}_cons {c |}{col 28}{res}{space 2}-.6421998{col 40}{space 2} .4397332{col 51}{space 1}   -1.46{col 60}{space 3}0.154{col 68}{space 4}-1.539042{col 81}{space 3} .2546419
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. matrix did_cpcs = r(table)
{txt}
{com}. scalar did_cpcs_n = e(N) / 2
{txt}
{com}. restore
{txt}
{com}. 
. // RSES
. preserve
{txt}
{com}. keep if RSES_std != .
{txt}(6 observations deleted)

{com}. gen outcome_1 = RSES_std
{txt}
{com}. gen outcome_0 = rosen_pre_std_missing_0
{txt}
{com}. reshape long outcome_, i(student_no) j(time)
{txt}(j = 0 1)

Data{col 36}Wide{col 43}->{col 48}Long
{hline 77}
Number of observations     {res}         190   {txt}->   {res}380         
{txt}Number of variables        {res}       1,273   {txt}->   {res}1,273       
{txt}j variable (2 values)                     ->   {res}time
{txt}xij variables:
                    {res}outcome_0 outcome_1   {txt}->   {res}outcome_
{txt}{hline 77}

{com}. reg outcome_ i.treatment##i.time DT_score_pre_std_missing_0 cpcs_pre_std_missing_0 `controls' [pweight=weight], cluster(school_no)
{txt}(sum of wgt is 763.0556974411011)

Linear regression                               Number of obs     = {res}       380
                                                {txt}F(20, 31)         =  {res}    51.58
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.3294
                                                {txt}Root MSE          =    {res} .88634

{txt}{ralign 92:(Std. err. adjusted for {res:32} clusters in {res:school_no})}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}                  outcome_{col 28}{c |} Coefficient{col 40}  std. err.{col 52}      t{col 60}   P>|t|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}1.treatment {c |}{col 28}{res}{space 2}  -.02326{col 40}{space 2} .1585712{col 51}{space 1}   -0.15{col 60}{space 3}0.884{col 68}{space 4}-.3466681{col 81}{space 3} .3001481
{txt}{space 20}1.time {c |}{col 28}{res}{space 2}-.2761303{col 40}{space 2} .1911747{col 51}{space 1}   -1.44{col 60}{space 3}0.159{col 68}{space 4}-.6660337{col 81}{space 3} .1137731
{txt}{space 26} {c |}
{space 12}treatment#time {c |}
{space 22}1 1  {c |}{col 28}{res}{space 2} .6639115{col 40}{space 2}  .346522{col 51}{space 1}    1.92{col 60}{space 3}0.065{col 68}{space 4}-.0428249{col 81}{space 3} 1.370648
{txt}{space 26} {c |}
DT_score_pre_std_missing_0 {c |}{col 28}{res}{space 2}-.0810581{col 40}{space 2} .0372209{col 51}{space 1}   -2.18{col 60}{space 3}0.037{col 68}{space 4}-.1569706{col 81}{space 3}-.0051456
{txt}{space 4}cpcs_pre_std_missing_0 {c |}{col 28}{res}{space 2}  .429283{col 40}{space 2}  .033655{col 51}{space 1}   12.76{col 60}{space 3}0.000{col 68}{space 4} .3606432{col 81}{space 3} .4979229
{txt}{space 19}4.grade {c |}{col 28}{res}{space 2}-.1053845{col 40}{space 2} .0929772{col 51}{space 1}   -1.13{col 60}{space 3}0.266{col 68}{space 4}-.2950127{col 81}{space 3} .0842437
{txt}{space 22}gend {c |}{col 28}{res}{space 2} .1090338{col 40}{space 2} .1067164{col 51}{space 1}    1.02{col 60}{space 3}0.315{col 68}{space 4}-.1086157{col 81}{space 3} .3266834
{txt}{space 19}branch1 {c |}{col 28}{res}{space 2} .0885401{col 40}{space 2} .1884167{col 51}{space 1}    0.47{col 60}{space 3}0.642{col 68}{space 4}-.2957382{col 81}{space 3} .4728184
{txt}{space 19}branch2 {c |}{col 28}{res}{space 2} .2377371{col 40}{space 2} .2405986{col 51}{space 1}    0.99{col 60}{space 3}0.331{col 68}{space 4}-.2529669{col 81}{space 3} .7284411
{txt}{space 19}branch3 {c |}{col 28}{res}{space 2} .4765103{col 40}{space 2} .1940419{col 51}{space 1}    2.46{col 60}{space 3}0.020{col 68}{space 4} .0807592{col 81}{space 3} .8722614
{txt}{space 12}income_source1 {c |}{col 28}{res}{space 2} .1474953{col 40}{space 2} .4599784{col 51}{space 1}    0.32{col 60}{space 3}0.751{col 68}{space 4}-.7906368{col 81}{space 3} 1.085627
{txt}{space 12}income_source2 {c |}{col 28}{res}{space 2} .4272887{col 40}{space 2} .3351639{col 51}{space 1}    1.27{col 60}{space 3}0.212{col 68}{space 4}-.2562827{col 81}{space 3}  1.11086
{txt}{space 12}income_source3 {c |}{col 28}{res}{space 2}  .369376{col 40}{space 2}  .345121{col 51}{space 1}    1.07{col 60}{space 3}0.293{col 68}{space 4}-.3345029{col 81}{space 3} 1.073255
{txt}{space 12}income_source4 {c |}{col 28}{res}{space 2}-.9415119{col 40}{space 2} 1.075117{col 51}{space 1}   -0.88{col 60}{space 3}0.388{col 68}{space 4}-3.134228{col 81}{space 3} 1.251204
{txt}{space 4}last_income_per_member {c |}{col 28}{res}{space 2}-.0000143{col 40}{space 2} .0000227{col 51}{space 1}   -0.63{col 60}{space 3}0.533{col 68}{space 4}-.0000605{col 81}{space 3} .0000319
{txt}{space 18}hhmember {c |}{col 28}{res}{space 2} .0092744{col 40}{space 2} .0351002{col 51}{space 1}    0.26{col 60}{space 3}0.793{col 68}{space 4}-.0623129{col 81}{space 3} .0808618
{txt}{space 17}hhheadage {c |}{col 28}{res}{space 2}-.0022024{col 40}{space 2} .0061978{col 51}{space 1}   -0.36{col 60}{space 3}0.725{col 68}{space 4}-.0148429{col 81}{space 3} .0104382
{txt}{space 13}hhheadeduyear {c |}{col 28}{res}{space 2}-.0134922{col 40}{space 2} .0132536{col 51}{space 1}   -1.02{col 60}{space 3}0.317{col 68}{space 4}-.0405231{col 81}{space 3} .0135388
{txt}{space 18}age_tchr {c |}{col 28}{res}{space 2}-.0054481{col 40}{space 2} .0088399{col 51}{space 1}   -0.62{col 60}{space 3}0.542{col 68}{space 4}-.0234772{col 81}{space 3} .0125811
{txt}{space 14}phone_survey {c |}{col 28}{res}{space 2}-.0716804{col 40}{space 2} .1013371{col 51}{space 1}   -0.71{col 60}{space 3}0.485{col 68}{space 4}-.2783587{col 81}{space 3}  .134998
{txt}{space 21}_cons {c |}{col 28}{res}{space 2}-.3630497{col 40}{space 2} .4854085{col 51}{space 1}   -0.75{col 60}{space 3}0.460{col 68}{space 4}-1.353047{col 81}{space 3} .6269474
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. matrix did_rses = r(table)
{txt}
{com}. scalar did_rses_n = e(N) / 2
{txt}
{com}. restore
{txt}
{com}. 
. 
. // significant level
. 
. local outcome cog rses cpcs
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. forvalues i = 1/9 {c -(}
{txt}  3{com}.                 if did_`dep'[4,`i']<=0.01 {c -(}
{txt}  4{com}.                         local star_`dep'_`i' %3s "***"
{txt}  5{com}.                 {c )-}
{txt}  6{com}.                 else if (did_`dep'[4,`i']>0.01) & (did_`dep'[4,`i']<=0.05) {c -(}
{txt}  7{com}.                         local star_`dep'_`i' %2s "**"
{txt}  8{com}.                 {c )-}
{txt}  9{com}.                 else if (did_`dep'[4,`i']>0.05) & (did_`dep'[4,`i']<=0.10) {c -(}
{txt} 10{com}.                         local star_`dep'_`i' %1s "*"
{txt} 11{com}.                 {c )-}
{txt} 12{com}.                 else {c -(}
{txt} 13{com}.                         local star_`dep'_`i'  ""
{txt} 14{com}.                 {c )-}
{txt} 15{com}. {c )-}
{txt} 16{com}. {c )-} 
{txt}
{com}. 
. /// Table
> tempname hh2
{txt}
{com}. file open `hh2' using "$path_output/did_matchsample_ipw.tex", write replace
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "% Author: Kazuma Takakura" _newline
{txt}
{com}. file write `hh2' "% Date: `c(current_date)'" _newline
{txt}
{com}. file write `hh2' "% Time: `c(current_time)'" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. 
. file write `hh2' "\begin{c -(}table{c )-}[h!]\footnotesize" _newline
{txt}
{com}. file write `hh2' "  \centering" _newline
{txt}
{com}. file write `hh2' "  \caption{c -(}Inverse Probability Weighted Difference in Differences (Matched Sample){c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:did_psm_ipw{c )-}" _newline
{txt}
{com}. file write `hh2' "\scalebox{c -(}1{c )-}{c -(}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}threeparttable{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "\begin{c -(}tabular{c )-}{c -(}lccc{c )-}\toprule" _newline
{txt}
{com}. 
.   
. file write `hh2' "  & Rapid math test score &  RSES score  & CPCS score   \\\midrule\midrule" _newline
{txt}
{com}. 
. file write `hh2' "  DiD & " %04.3f (did_cog[1,8]) `star_cog_8' "  & " %04.3f (did_rses[1,8]) `star_rses_8' " & " %04.3f (did_cpcs[1,8]) `star_cpcs_8' " \\ " _newline
{txt}
{com}. file write `hh2' "    & (" %04.3f (did_cog[2,8]) ") & (" %04.3f (did_rses[2,8]) ") & (" %04.3f (did_cpcs[2,8]) ") \\ " _newline
{txt}
{com}. 
. file write `hh2' "  Treatment & " %04.3f (did_cog[1,2]) `star_cog_2' "  & " %04.3f (did_rses[1,2]) `star_rses_2' " & " %04.3f (did_cpcs[1,2]) `star_cpcs_2' " \\ " _newline
{txt}
{com}. file write `hh2' "    & (" %04.3f (did_cog[2,2]) ") & (" %04.3f (did_rses[2,2]) ") & (" %04.3f (did_cpcs[2,2]) ") \\ " _newline
{txt}
{com}. 
. file write `hh2' "  After & " %04.3f (did_cog[1,4]) `star_cog_4' "  & " %04.3f (did_rses[1,4]) `star_rses_4' " & " %04.3f (did_cpcs[1,4]) `star_cpcs_4' " \\ " _newline
{txt}
{com}. file write `hh2' "    & (" %04.3f (did_cog[2,4]) ") & (" %04.3f (did_rses[2,4]) ") & (" %04.3f (did_cpcs[2,4]) ") \\ " _newline
{txt}
{com}. 
. file write `hh2' "  Constant & " %04.3f (did_cog[1,9]) `star_cog_9' "  & " %04.3f (did_rses[1,9]) `star_rses_4' " & " %04.3f (did_cpcs[1,9]) `star_cpcs_9' " \\ " _newline
{txt}
{com}. file write `hh2' "    & (" %04.3f (did_cog[2,9]) ") & (" %04.3f (did_rses[2,9]) ") & (" %04.3f (did_cpcs[2,9]) ") \\ " _newline
{txt}
{com}. 
. file write `hh2' "  Observations & " (did_cog_n) "  & " (did_rses_n) " & " (did_cpcs_n) " \\ " _newline
{txt}
{com}. 
. file write `hh2' "\midrule" _newline
{txt}
{com}. file write `hh2' "\end{c -(}tabular{c )-}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\item (a) We control student's grade, sex, baseline cognitive and baseline non-cognitive score, DT baseline time, branch dummy (location), parents' income source, last income per family member, number of household members, age of household head, education level of household head, teacher's age, sex, and phone survey dummy. The same variables are used for propensity score calculation." _newline
{txt}
{com}. file write `hh2' "\item (b) School-clustered standard errors are reported within parentheses. " _newline
{txt}
{com}. file write `hh2' "\item (c) $^*$ Significant at 10\% level; $^{c -(}**{c )-}$ significant at 5\% level; $^{c -(}***{c )-}$ significant at 1\% level. " _newline
{txt}
{com}. file write `hh2' "\end{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}threeparttable{c )-}" _newline
{txt}
{com}. file write `hh2' "{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:addlabel{c )-}%" _newline
{txt}
{com}. file write `hh2' "\end{c -(}table{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. file close `hh2'
{txt}
{com}. 
{txt}end of do-file

{com}. 
. 
. * log close
. 
. 
. 
. 
. 
{txt}end of do-file

{com}. do "/var/folders/mb/s9qljd594gbfyhl2dqnnwlcw0000gn/T//SD56186.000000"
{txt}
{com}. * This is the do file to create "Table 1. Summary Statistics"
. set seed 12345
{txt}
{com}. 
. use "$path_data/temp/followup_student_parents_matched", clear
{txt}
{com}. 
. corr rosen_pre_std cpcs_pre_std
{txt}(obs=243)

             {c |} rosen_~d cpcs_p~d
{hline 13}{c +}{hline 18}
rosen_pre_~d {c |}{res}   1.0000
{txt}cpcs_pre_std {c |}{res}   0.9026   1.0000

{txt}
{com}. corr RSES_std CPCS_std
{txt}(obs=236)

             {c |} RSES_std CPCS_std
{hline 13}{c +}{hline 18}
    RSES_std {c |}{res}   1.0000
    {txt}CPCS_std {c |}{res}   0.9701   1.0000

{txt}
{com}. 
. 
. /// Varable Selection
> /// Baseline
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat DT_score_pre_std rosen_pre_std cpcs_pre_std if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_bl = r(StatTotal)
{txt}  5{com}. 
. tabstat DT_score_pre_std rosen_pre_std cpcs_pre_std if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_bl = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}      144       145       145
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   DT_score_p~d  rosen_pre_~d  cpcs_pre_std
N {res}          144           145           145
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}       95        98        98
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   DT_score_p~d  rosen_pre_~d  cpcs_pre_std
N {res}           95            98            98
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res}-.0313509  .0382918  .1345164
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      DT_score_p~d  rosen_pre_~d  cpcs_pre_std
Mean {res}   -.03135095     .03829184      .1345164
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .0475214 -.0566567 -.1990291
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      DT_score_p~d  rosen_pre_~d  cpcs_pre_std
Mean {res}    .04752144    -.05665667    -.19902912
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} 1.023177  .9748496  .9271749
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    DT_score_p~d  rosen_pre_~d  cpcs_pre_std
SD {res}    1.0231772     .97484957     .92717486
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} .9672202  1.038561  1.073121
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    DT_score_p~d  rosen_pre_~d  cpcs_pre_std
SD {res}    .96722024      1.038561     1.0731214
{reset}
{com}. 
. matrix n_bl = J(1,3,.)
{txt}
{com}. forvalues i = 1/3 {c -(}
{txt}  2{com}.         matrix n_bl[1,`i'] = n_tr_bl[1,`i'] + n_ct_bl[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in DT_score_pre_std rosen_pre_std cpcs_pre_std{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}239
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 32
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  2
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.5
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        DT_score_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0788724{col 38}{space 1}  -0.38{col 46}{space 3}0.710{col 54}{space 3}-.5310091{col 66}{space 3} .3817528
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}           rosen_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0949485{col 38}{space 1}   0.47{col 46}{space 3}0.694{col 54}{space 3}-.3284004{col 66}{space 3} .5283027
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}            cpcs_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3335455{col 38}{space 1}   1.82{col 46}{space 3}0.098{col 54}{space 3}-.0768279{col 66}{space 3} .7039806
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. /// Family
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat hhmember hhheadage hhheadeduyear if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_parent = r(StatTotal)
{txt}  5{com}. 
. tabstat hhmember hhheadage hhheadeduyear if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_parent = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}      145       145       145
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
       hhmember     hhheadage  hhheadeduy~r
N {res}          145           145           145
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}       98        98        98
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
       hhmember     hhheadage  hhheadeduy~r
N {res}           98            98            98
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res} 4.510345  46.57241  2.331034
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
          hhmember     hhheadage  hhheadeduy~r
Mean {res}    4.5103448     46.572414     2.3310345
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res} 4.265306  46.68878  3.163265
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
          hhmember     hhheadage  hhheadeduy~r
Mean {res}    4.2653061     46.688776     3.1632653
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} 1.280827   9.03907  2.995495
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
        hhmember     hhheadage  hhheadeduy~r
SD {res}    1.2808268     9.0390702     2.9954947
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} 1.197515  9.408681  3.530993
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
        hhmember     hhheadage  hhheadeduy~r
SD {res}    1.1975148     9.4086808     3.5309935
{reset}
{com}. 
. matrix n_parent = J(1,3,.)
{txt}
{com}. forvalues i = 1/3 {c -(}
{txt}  2{com}.         matrix n_parent[1,`i'] = n_tr_parent[1,`i'] + n_ct_parent[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in hhmember hhheadage hhheadeduyear{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                hhmember{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2450387{col 38}{space 1}   1.29{col 46}{space 3}0.210{col 54}{space 3}-.1456223{col 66}{space 3} .6501634
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}               hhheadage{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.1163617{col 38}{space 1}  -0.07{col 46}{space 3}0.948{col 54}{space 3}-3.391366{col 66}{space 3} 3.065347
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}           hhheadeduyear{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.8322308{col 38}{space 1}  -2.22{col 46}{space 3}0.054{col 54}{space 3}-1.652465{col 66}{space 3} .0168459
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. 
. 
. /// School　attendance
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat q2a q2b q2c q2h if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_school = r(StatTotal)
{txt}  5{com}. 
. tabstat q2a q2b q2c q2h if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_school = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:N} {...}
{c |}{...}
 {res}      145       145       145       145
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
   q2a  q2b  q2c  q2h
N {res} 145  145  145  145
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:N} {...}
{c |}{...}
 {res}       98        98        98        98
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
   q2a  q2b  q2c  q2h
N {res}  98   98   98   98
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .5517241  9.606897   .062069  .3793103
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
            q2a        q2b        q2c        q2h
Mean {res} .55172414  9.6068966  .06206897  .37931034
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .5306122  9.602041  .0408163  .4489796
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
            q2a        q2b        q2c        q2h
Mean {res} .53061224  9.6020408  .04081633  .44897959
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:SD} {...}
{c |}{...}
 {res} .4990412  1.029405  .2421171  .4868973
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
          q2a        q2b        q2c        q2h
SD {res} .49904123  1.0294048   .2421171  .48689728
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:SD} {...}
{c |}{...}
 {res} .5016279  .8703571  .1988818  .4999474
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
          q2a        q2b        q2c        q2h
SD {res}  .5016279  .87035715  .19888179   .4999474
{reset}
{com}. 
. matrix n_school = J(1,4,.)
{txt}
{com}. forvalues i = 1/4 {c -(}
{txt}  2{com}.         matrix n_school[1,`i'] = n_tr_school[1,`i'] + n_ct_school[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in q2a q2b q2c q2h{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                     q2a{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0211119{col 38}{space 1}   0.25{col 46}{space 3}0.806{col 54}{space 3}-.1527033{col 66}{space 3} .1936317
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                     q2b{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0048557{col 38}{space 1}   0.03{col 46}{space 3}0.952{col 54}{space 3}-.3697136{col 66}{space 3}  .377903
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                     q2c{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0212526{col 38}{space 1}   0.56{col 46}{space 3}0.592{col 54}{space 3}-.0544197{col 66}{space 3}  .097161
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                     q2h{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0696692{col 38}{space 1}  -0.85{col 46}{space 3}0.398{col 54}{space 3}-.2536538{col 66}{space 3} .0977858
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. /// Other study variable
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat tutor study_other study_affect_covid hometutoring onlineclass studymyself parentsteach if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_study = r(StatTotal)
{txt}  5{com}. 
. tabstat tutor study_other study_affect_covid hometutoring onlineclass studymyself parentsteach if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_study = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:N} {...}
{c |}{...}
 {res}      145       145       145       145       145       145       145
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
          tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
N {res}          145           145           145           145           145           145           145
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:N} {...}
{c |}{...}
 {res}       98        98        98        98        98        98        98
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
          tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
N {res}           98            98            98            98            98            98            98
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:Mean} {...}
{c |}{...}
 {res}  .337931   .462069  .6482759  .0965517  .0482759  .5241379  .0275862
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
             tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
Mean {res}    .33793103     .46206897     .64827586     .09655172     .04827586     .52413793     .02758621
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .4591837  .4285714  .6020408  .1428571  .1326531  .5306122  .1938776
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
             tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
Mean {res}    .45918367     .42857143     .60204082     .14285714     .13265306     .53061224     .19387755
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:SD} {...}
{c |}{...}
 {res} .4746445  .5002873  .4791635  .2963701  .2150915  .5011481  .1643517
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
           tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
SD {res}    .47464445     .50028727     .47916354     .29637012     .21509153     .50114811     .16435174
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:SD} {...}
{c |}{...}
 {res} .5008934   .497416  .4919935  .3517262  .3409434  .5016279  .3973667
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
           tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
SD {res}    .50089337       .497416     .49199354     .35172623     .34094336      .5016279     .39736667
{reset}
{com}. 
. matrix n_study = J(1,8,.)
{txt}
{com}. forvalues i = 1/8 {c -(}
{txt}  2{com}.         matrix n_study[1,`i'] = n_tr_study[1,`i'] + n_ct_study[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in tutor study_other study_affect_covid hometutoring onlineclass studymyself parentsteach{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                   tutor{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.1212526{col 38}{space 1}  -1.69{col 46}{space 3}0.126{col 54}{space 3}-.2746526{col 66}{space 3} .0434892
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}             study_other{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0334975{col 38}{space 1}   0.39{col 46}{space 3}0.728{col 54}{space 3}-.1614579{col 66}{space 3} .2143565
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}      study_affect_covid{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  .046235{col 38}{space 1}   0.56{col 46}{space 3}0.552{col 54}{space 3}-.1135231{col 66}{space 3} .2299758
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}            hometutoring{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0463054{col 38}{space 1}  -1.11{col 46}{space 3}0.304{col 54}{space 3}-.1344873{col 66}{space 3} .0492743
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}             onlineclass{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0843772{col 38}{space 1}  -1.92{col 46}{space 3}0.076{col 54}{space 3}-.1845307{col 66}{space 3} .0112695
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}             studymyself{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0064743{col 38}{space 1}  -0.08{col 46}{space 3}0.922{col 54}{space 3}  -.18756{col 66}{space 3}  .188333
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}            parentsteach{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.1662913{col 38}{space 1}  -3.85{col 46}{space 3}0.002{col 54}{space 3}-.2570312{col 66}{space 3}-.0641783
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. /// Cognitive
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat followup_cog_std if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_cog = r(StatTotal)
{txt}  5{com}. 
. tabstat followup_cog_std if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_cog = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}{ralign 12:Variable} {...}
{c |}         N
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res}      145
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
   followup_c~d
N {res}          145
{reset}
{ralign 12:Variable} {...}
{c |}         N
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res}       98
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
   followup_c~d
N {res}           98
{reset}
{ralign 12:Variable} {...}
{c |}      Mean
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res}-.0920409
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
      followup_c~d
Mean {res}   -.09204085
{reset}
{ralign 12:Variable} {...}
{c |}      Mean
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res} .1361831
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
      followup_c~d
Mean {res}    .13618309
{reset}
{ralign 12:Variable} {...}
{c |}        SD
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res} 1.070796
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
    followup_c~d
SD {res}     1.070796
{reset}
{ralign 12:Variable} {...}
{c |}        SD
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res} .8725076
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
    followup_c~d
SD {res}    .87250763
{reset}
{com}. 
. matrix n_cog = J(1,1,.)
{txt}
{com}. forvalues i = 1/1 {c -(}
{txt}  2{com}.         matrix n_cog[1,`i'] = n_tr_cog[1,`i'] + n_ct_cog[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2282239{col 38}{space 1}  -1.36{col 46}{space 3}0.212{col 54}{space 3}-.5649801{col 66}{space 3} .1204623
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}.         
. matrix r2_followup_cog_std_temp = r(table)
{txt}
{com}. 
. 
. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix r2_followup_cog_std_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix r2_followup_cog_std_mean[1,`j'] = r2_followup_cog_std_temp[1,`j']
{txt}  3{com}. * standard error
. * matrix r2_followup_cog_std_se[1,`j'] = r2_followup_cog_std_temp[2,`j']
. * p value
. matrix r2_followup_cog_std_pv[1,`j'] = r2_followup_cog_std_temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}.     
. /// Non cognitive
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat followup_noncog_std RSES_std CPCS_std if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_noncog = r(StatTotal)
{txt}  5{com}. 
. tabstat followup_noncog_std RSES_std CPCS_std if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_noncog = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}      105       140       140
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   followup_n~d      RSES_std      CPCS_std
N {res}          105           140           140
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}       74        96        96
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   followup_n~d      RSES_std      CPCS_std
N {res}           74            96            96
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .1969319  .1591241  .1745941
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      followup_n~d      RSES_std      CPCS_std
Mean {res}    .19693189      .1591241     .17459415
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res}-.2794302 -.2320565  -.254617
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      followup_n~d      RSES_std      CPCS_std
Mean {res}   -.27943024    -.23205648    -.25461705
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} 1.006158  1.022691  1.008304
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    followup_n~d      RSES_std      CPCS_std
SD {res}    1.0061577     1.0226907     1.0083041
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} .9279901  .9228443  .9357831
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    followup_n~d      RSES_std      CPCS_std
SD {res}    .92799012     .92284427     .93578307
{reset}
{com}. 
. matrix n_noncog = J(1,3,.)
{txt}
{com}. forvalues i = 1/3 {c -(}
{txt}  2{com}.         matrix n_noncog[1,`i'] = n_tr_noncog[1,`i'] + n_ct_noncog[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in followup_noncog_std RSES_std CPCS_std{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}179
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 32
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}5.6
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}     followup_noncog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .4763621{col 38}{space 1}   2.08{col 46}{space 3}0.072{col 54}{space 3}-.0408981{col 66}{space 3} .9422937
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:19})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}236
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.2
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3911806{col 38}{space 1}   2.02{col 46}{space 3}0.046{col 54}{space 3} .0096593{col 66}{space 3} .8006975
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}30{text}.{text} done{text} ({result:31})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}236
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.2
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .4292112{col 38}{space 1}   2.26{col 46}{space 3}0.052{col 54}{space 3}-.0096674{col 66}{space 3} .8056736
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. 
. /// Behavioral
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat hyper if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_hyper = r(StatTotal)
{txt}  5{com}. 
. tabstat hyper if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_hyper = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}{ralign 12:Variable} {...}
{c |}         N
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res}      113
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
   hyper
N {res}   113
{reset}
{ralign 12:Variable} {...}
{c |}         N
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res}       71
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
   hyper
N {res}    71
{reset}
{ralign 12:Variable} {...}
{c |}      Mean
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res} .2654867
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
          hyper
Mean {res} .26548673
{reset}
{ralign 12:Variable} {...}
{c |}      Mean
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res} .0704225
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
          hyper
Mean {res} .07042254
{reset}
{ralign 12:Variable} {...}
{c |}        SD
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res}  .443559
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
        hyper
SD {res} .44355905
{reset}
{ralign 12:Variable} {...}
{c |}        SD
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res} .2576789
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
        hyper
SD {res} .25767885
{reset}
{com}. 
. matrix n_hyper = J(1,1,.)
{txt}
{com}. forvalues i = 1/1 {c -(}
{txt}  2{com}.         matrix n_hyper[1,`i'] = n_tr_hyper[1,`i'] + n_ct_hyper[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in hyper{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment if hypernoinfo == 0, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:19})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}184
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}5.6
{col 69}{txt}max{col 72} = {res} 12
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                   hyper{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1950642{col 38}{space 1}   3.37{col 46}{space 3}0.006{col 54}{space 3} .0581447{col 66}{space 3} .3220425
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. 
. // significant level
. 
. local outcome DT_score_pre_std rosen_pre_std cpcs_pre_std hhmember hhheadage hhheadeduyear q2a q2c q2h tutor study_other followup_cog_std RSES_std CPCS_std
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}.                 if r2_`dep'_pv[1,1]<=0.01 {c -(}
{txt}  3{com}.                         local star_`dep' %3s "***"
{txt}  4{com}.                 {c )-}
{txt}  5{com}.                 else if (r2_`dep'_pv[1,1]>0.01) & (r2_`dep'_pv[1,1]<=0.05) {c -(}
{txt}  6{com}.                         local star_`dep' %2s "**"
{txt}  7{com}.                 {c )-}
{txt}  8{com}.                 else if (r2_`dep'_pv[1,1]>0.05) & (r2_`dep'_pv[1,1]<=0.10) {c -(}
{txt}  9{com}.                         local star_`dep' %1s "*"
{txt} 10{com}.                 {c )-}
{txt} 11{com}.                 else {c -(}
{txt} 12{com}.                         local star_`dep'  ""
{txt} 13{com}.                 {c )-}
{txt} 14{com}. {c )-} 
{txt}
{com}. 
. rwolf DT_score_pre_std rosen_pre_std cpcs_pre_std hhmember hhheadage hhheadeduyear, indepvar(treatment) reps(1000)
Bootstrap replications (1000). This may take some time.
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Romano-Wolf step-down adjusted p-values


Independent variable:  treatment
Outcome variables:   DT_score_pre_std rosen_pre_std cpcs_pre_std hhmember
{col 22}hhheadage hhheadeduyear
Number of resamples: 1000


{hline 78}
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
{hline 20}+{hline 57}
   {txt}DT_score_pre_std {c |}    {res}0.5518             0.5564              0.8511
      {txt}rosen_pre_std {c |}    {res}0.4689             0.4585              0.8511
       {txt}cpcs_pre_std {c |}    {res}0.0105             0.0180              0.0659
           {txt}hhmember {c |}    {res}0.1345             0.1159              0.4296
          {txt}hhheadage {c |}    {res}0.9229             0.9201              0.9201
      {txt}hhheadeduyear {c |}    {res}0.0494             0.0599              0.2258
{hline 78}
{txt}
{com}. rwolf q2a q2c q2h tutor study_other followup_cog_std RSES_std CPCS_std, indepvar(treatment) reps(1000)
Bootstrap replications (1000). This may take some time.
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Romano-Wolf step-down adjusted p-values


Independent variable:  treatment
Outcome variables:   q2a q2c q2h tutor study_other followup_cog_std RSES_std CPCS_std
Number of resamples: 1000


{hline 78}
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
{hline 20}+{hline 57}
                {txt}q2a {c |}    {res}0.7471             0.7443              0.8012
                {txt}q2c {c |}    {res}0.4722             0.4685              0.8012
                {txt}q2h {c |}    {res}0.2801             0.2687              0.5974
              {txt}tutor {c |}    {res}0.0573             0.0500              0.1968
        {txt}study_other {c |}    {res}0.6083             0.5944              0.8012
   {txt}followup_cog_std {c |}    {res}0.0809             0.0779              0.2418
           {txt}RSES_std {c |}    {res}0.0030             0.0060              0.0170
           {txt}CPCS_std {c |}    {res}0.0011             0.0040              0.0080
{hline 78}
{txt}
{com}. 
. 
. /// Table
> tempname hh2
{txt}
{com}. file open `hh2' using "$path_output/summary_stat.tex", write replace
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "% Author: Kazuma Takakura" _newline
{txt}
{com}. file write `hh2' "% Date: `c(current_date)'" _newline
{txt}
{com}. file write `hh2' "% Time: `c(current_time)'" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. 
. file write `hh2' "\begin{c -(}table{c )-}[h!]\footnotesize" _newline
{txt}
{com}. file write `hh2' "  \centering" _newline
{txt}
{com}. file write `hh2' "  \caption{c -(}Summary Statistics{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:sumstat{c )-}" _newline
{txt}
{com}. file write `hh2' "\scalebox{c -(}1{c )-}{c -(}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}threeparttable{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "\begin{c -(}tabular{c )-}{c -(}lccccc{c )-}\toprule" _newline
{txt}
{com}. 
.   
. file write `hh2' " Dependent Variable & Treatment &  Control  & Difference & N   \\\midrule\midrule" _newline
{txt}
{com}. file write `hh2' " Panel A: Baseline & & & &   \\ " _newline
{txt}
{com}. file write `hh2' " DT score^{c -(}a{c )-} & " %04.3f (mean_tr_bl[1,1]) " & " %04.3f (mean_ct_bl[1,1]) " & " %04.3f (r2_DT_score_pre_std_mean[1,1]) `star_DT_score_pre_std' " & " (n_bl[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_bl[1,1]) " ] & [ " %04.3f (sd_ct_bl[1,1]) " ] & ( " %04.3f (r2_DT_score_pre_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.831) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. 
. file write `hh2' " RSES score^{c -(}a{c )-} & " %04.3f (mean_tr_bl[1,2]) " & " %04.3f (mean_ct_bl[1,2]) " & " %04.3f (r2_rosen_pre_std_mean[1,1]) `star_rosen_pre_std' " & "  (n_bl[1,2]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_bl[1,2]) " ] & [ " %04.3f (sd_ct_bl[1,2]) " ] & ( " %04.3f (r2_rosen_pre_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.831) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " CPCS score^{c -(}a{c )-} & " %04.3f (mean_tr_bl[1,3]) " & " %04.3f (mean_ct_bl[1,3]) " & " %04.3f (r2_cpcs_pre_std_mean[1,1]) `star_cpcs_pre_std' " & "  (n_bl[1,3]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_bl[1,3]) " ] & [ " %04.3f (sd_ct_bl[1,3]) " ] & ( " %04.3f (r2_cpcs_pre_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.059) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Household size & " %04.3f (mean_tr_parent[1,1]) " & " %04.3f (mean_ct_parent[1,1]) " & " %04.3f (r2_hhmember_mean[1,1]) `star_hhmember'  " & " (n_parent[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_parent[1,1]) " ] & [ " %04.3f (sd_ct_parent[1,1]) " ] & ( " %04.3f (r2_hhmember_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.464) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Household head age & " %04.3f (mean_tr_parent[1,2]) " & " %04.3f (mean_ct_parent[1,2]) " & " %04.3f (r2_hhheadage_mean[1,1]) `star_hhheadage' " & "  (n_parent[1,2]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_parent[1,2]) " ] & [ " %04.3f (sd_ct_parent[1,2]) " ] & ( " %04.3f (r2_hhheadage_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.920) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Household head education & " %04.3f (mean_tr_parent[1,3]) " & " %04.3f (mean_ct_parent[1,3]) " & " %04.3f (r2_hhheadeduyear_mean[1,1]) `star_hhheadeduyear' " & "  (n_parent[1,3]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_parent[1,3]) " ] & [ " %04.3f (sd_ct_parent[1,3]) " ] & ( " %04.3f (r2_hhheadeduyear_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.248) " \{c )-} &   \\ " _newline
{txt}
{com}. file write `hh2' " \\ "_newline
{txt}
{com}. 
. file write `hh2' " Panel B: Follow-up & & & &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " School attendance & " %04.3f (mean_tr_school[1,1]) " & " %04.3f (mean_ct_school[1,1]) " & " %04.3f (r2_q2a_mean[1,1]) `star_q2a' " & " (n_school[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_school[1,1]) " ] & [ " %04.3f (sd_ct_school[1,1]) " ] & ( " %04.3f (r2_q2a_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.787) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Grade repeat & " %04.3f (mean_tr_school[1,3]) " & " %04.3f (mean_ct_school[1,3]) " & " %04.3f (r2_q2c_mean[1,1]) `star_q2c' " & "  (n_school[1,3]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_school[1,3]) " ] & [ " %04.3f (sd_ct_school[1,3]) " ] & ( " %04.3f (r2_q2c_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.787) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Drop out & " %04.3f (mean_tr_school[1,4]) " & " %04.3f (mean_ct_school[1,4]) " & " %04.3f (r2_q2h_mean[1,1]) `star_q2h'  " & "  (n_school[1,4]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_school[1,4]) " ] & [ " %04.3f (sd_ct_school[1,4]) " ] & ( " %04.3f (r2_q2h_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.576) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Tutoring & " %04.3f (mean_tr_study[1,1]) " & " %04.3f (mean_ct_study[1,1]) " & " %04.3f (r2_tutor_mean[1,1]) `star_tutor'  " & " (n_study[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_study[1,1]) " ] & [ " %04.3f (sd_ct_study[1,1]) " ] & ( " %04.3f (r2_tutor_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.230) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Self-study & " %04.3f (mean_tr_study[1,2]) " & " %04.3f (mean_ct_study[1,2]) " & " %04.3f (r2_study_other_mean[1,1]) `star_study_other' " & "  (n_study[1,2]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_study[1,2]) " ] & [ " %04.3f (sd_ct_study[1,2]) " ] & ( " %04.3f (r2_study_other_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.787) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Rapid math test score^{c -(}a{c )-} & " %04.3f (mean_tr_cog[1,1]) " & " %04.3f (mean_ct_cog[1,1]) " & " %04.3f (r2_followup_cog_std_mean[1,1]) `star_followup_cog_std'  "  & "  (n_cog[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_cog[1,1]) " ] & [ " %04.3f (sd_ct_cog[1,1]) " ] & ( " %04.3f (r2_followup_cog_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.270) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " RSES score^{c -(}a{c )-} & " %04.3f (mean_tr_noncog[1,2]) " & " %04.3f (mean_ct_noncog[1,2]) " & " %04.3f (r2_RSES_std_mean[1,1])   `star_RSES_std' " & " (n_noncog[1,2]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_noncog[1,2]) " ] & [ " %04.3f (sd_ct_noncog[1,2]) " ] & ( " %04.3f (r2_RSES_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.011) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " CPCS score^{c -(}a{c )-} & " %04.3f (mean_tr_noncog[1,3]) " & " %04.3f (mean_ct_noncog[1,3]) " & " %04.3f (r2_CPCS_std_mean[1,1])   `star_CPCS_std' "&  " (n_noncog[1,3]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_noncog[1,3]) " ] & [ " %04.3f (sd_ct_noncog[1,3]) " ] & ( " %04.3f (r2_CPCS_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.006) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. 
. file write `hh2' "\midrule" _newline
{txt}
{com}. file write `hh2' "\end{c -(}tabular{c )-}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\item (a) Variables are standardized using the average and variance of the whole follow-up sample in the February 2022 survey. " _newline
{txt}
{com}. file write `hh2' "\item (b) Standard deviations are reported in square brackets.  " _newline
{txt}
{com}. file write `hh2' "\item (c) Wild clustered bootstrap p-values are reported within parentheses. Clusters are schools at the baseline. There are 34 clusters. " _newline
{txt}
{com}. file write `hh2' "\item (d) Romano-Wolf multiple hypothesis testing p-values are reported in curly brackets. This test is conducted separately for the baseline variables and the follow-up variables." _newline
{txt}
{com}. file write `hh2' "\item (e) Statistical significance is indicated by stars based on the wild clustered bootstrap p-values reported in parentheses: $*$ denotes significance at the 10\% level, $∗∗$ at the 5\% level, and $∗∗∗$ at the 1\% level.  " _newline
{txt}
{com}. file write `hh2' "\end{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}threeparttable{c )-}" _newline
{txt}
{com}. file write `hh2' "{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}table{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. file close `hh2'
{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/mb/s9qljd594gbfyhl2dqnnwlcw0000gn/T//SD56186.000000"
{txt}
{com}. * This is the do file to create "Table 1. Summary Statistics"
. set seed 1234
{txt}
{com}. 
. use "$path_data/temp/followup_student_parents_matched", clear
{txt}
{com}. 
. corr rosen_pre_std cpcs_pre_std
{txt}(obs=243)

             {c |} rosen_~d cpcs_p~d
{hline 13}{c +}{hline 18}
rosen_pre_~d {c |}{res}   1.0000
{txt}cpcs_pre_std {c |}{res}   0.9026   1.0000

{txt}
{com}. corr RSES_std CPCS_std
{txt}(obs=236)

             {c |} RSES_std CPCS_std
{hline 13}{c +}{hline 18}
    RSES_std {c |}{res}   1.0000
    {txt}CPCS_std {c |}{res}   0.9701   1.0000

{txt}
{com}. 
. 
. /// Varable Selection
> /// Baseline
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat DT_score_pre_std rosen_pre_std cpcs_pre_std if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_bl = r(StatTotal)
{txt}  5{com}. 
. tabstat DT_score_pre_std rosen_pre_std cpcs_pre_std if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_bl = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}      144       145       145
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   DT_score_p~d  rosen_pre_~d  cpcs_pre_std
N {res}          144           145           145
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}       95        98        98
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   DT_score_p~d  rosen_pre_~d  cpcs_pre_std
N {res}           95            98            98
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res}-.0313509  .0382918  .1345164
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      DT_score_p~d  rosen_pre_~d  cpcs_pre_std
Mean {res}   -.03135095     .03829184      .1345164
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .0475214 -.0566567 -.1990291
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      DT_score_p~d  rosen_pre_~d  cpcs_pre_std
Mean {res}    .04752144    -.05665667    -.19902912
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} 1.023177  .9748496  .9271749
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    DT_score_p~d  rosen_pre_~d  cpcs_pre_std
SD {res}    1.0231772     .97484957     .92717486
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} .9672202  1.038561  1.073121
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    DT_score_p~d  rosen_pre_~d  cpcs_pre_std
SD {res}    .96722024      1.038561     1.0731214
{reset}
{com}. 
. matrix n_bl = J(1,3,.)
{txt}
{com}. forvalues i = 1/3 {c -(}
{txt}  2{com}.         matrix n_bl[1,`i'] = n_tr_bl[1,`i'] + n_ct_bl[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in DT_score_pre_std rosen_pre_std cpcs_pre_std{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}239
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 32
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  2
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.5
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        DT_score_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0788724{col 38}{space 1}  -0.38{col 46}{space 3}0.686{col 54}{space 3}-.5078236{col 66}{space 3} .3679694
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}           rosen_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0949485{col 38}{space 1}   0.47{col 46}{space 3}0.630{col 54}{space 3}-.3176773{col 66}{space 3} .5220605
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:29})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}            cpcs_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3335455{col 38}{space 1}   1.82{col 46}{space 3}0.084{col 54}{space 3} -.060877{col 66}{space 3} .7085159
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. /// Family
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat hhmember hhheadage hhheadeduyear if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_parent = r(StatTotal)
{txt}  5{com}. 
. tabstat hhmember hhheadage hhheadeduyear if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_parent = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}      145       145       145
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
       hhmember     hhheadage  hhheadeduy~r
N {res}          145           145           145
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}       98        98        98
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
       hhmember     hhheadage  hhheadeduy~r
N {res}           98            98            98
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res} 4.510345  46.57241  2.331034
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
          hhmember     hhheadage  hhheadeduy~r
Mean {res}    4.5103448     46.572414     2.3310345
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res} 4.265306  46.68878  3.163265
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
          hhmember     hhheadage  hhheadeduy~r
Mean {res}    4.2653061     46.688776     3.1632653
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} 1.280827   9.03907  2.995495
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
        hhmember     hhheadage  hhheadeduy~r
SD {res}    1.2808268     9.0390702     2.9954947
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} 1.197515  9.408681  3.530993
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
        hhmember     hhheadage  hhheadeduy~r
SD {res}    1.1975148     9.4086808     3.5309935
{reset}
{com}. 
. matrix n_parent = J(1,3,.)
{txt}
{com}. forvalues i = 1/3 {c -(}
{txt}  2{com}.         matrix n_parent[1,`i'] = n_tr_parent[1,`i'] + n_ct_parent[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in hhmember hhheadage hhheadeduyear{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                hhmember{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2450387{col 38}{space 1}   1.29{col 46}{space 3}0.180{col 54}{space 3}-.1467657{col 66}{space 3} .6292346
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}               hhheadage{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.1163617{col 38}{space 1}  -0.07{col 46}{space 3}0.968{col 54}{space 3}-2.888504{col 66}{space 3} 3.072887
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:19})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}           hhheadeduyear{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.8322308{col 38}{space 1}  -2.22{col 46}{space 3}0.042{col 54}{space 3}-1.580363{col 66}{space 3} -.018426
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. 
. 
. /// School　attendance
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat q2a q2b q2c q2h if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_school = r(StatTotal)
{txt}  5{com}. 
. tabstat q2a q2b q2c q2h if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_school = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:N} {...}
{c |}{...}
 {res}      145       145       145       145
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
   q2a  q2b  q2c  q2h
N {res} 145  145  145  145
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:N} {...}
{c |}{...}
 {res}       98        98        98        98
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
   q2a  q2b  q2c  q2h
N {res}  98   98   98   98
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .5517241  9.606897   .062069  .3793103
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
            q2a        q2b        q2c        q2h
Mean {res} .55172414  9.6068966  .06206897  .37931034
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .5306122  9.602041  .0408163  .4489796
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
            q2a        q2b        q2c        q2h
Mean {res} .53061224  9.6020408  .04081633  .44897959
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:SD} {...}
{c |}{...}
 {res} .4990412  1.029405  .2421171  .4868973
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
          q2a        q2b        q2c        q2h
SD {res} .49904123  1.0294048   .2421171  .48689728
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:SD} {...}
{c |}{...}
 {res} .5016279  .8703571  .1988818  .4999474
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
          q2a        q2b        q2c        q2h
SD {res}  .5016279  .87035715  .19888179   .4999474
{reset}
{com}. 
. matrix n_school = J(1,4,.)
{txt}
{com}. forvalues i = 1/4 {c -(}
{txt}  2{com}.         matrix n_school[1,`i'] = n_tr_school[1,`i'] + n_ct_school[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in q2a q2b q2c q2h{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:29})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                     q2a{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0211119{col 38}{space 1}   0.25{col 46}{space 3}0.766{col 54}{space 3}-.1436247{col 66}{space 3} .2076462
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                     q2b{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0048557{col 38}{space 1}   0.03{col 46}{space 3}0.964{col 54}{space 3}-.3878393{col 66}{space 3}  .357507
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                     q2c{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0212526{col 38}{space 1}   0.56{col 46}{space 3}0.638{col 54}{space 3}-.0560825{col 66}{space 3} .0932379
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                     q2h{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0696692{col 38}{space 1}  -0.85{col 46}{space 3}0.418{col 54}{space 3} -.242757{col 66}{space 3} .1087454
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. /// Other study variable
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat tutor study_other study_affect_covid hometutoring onlineclass studymyself parentsteach if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_study = r(StatTotal)
{txt}  5{com}. 
. tabstat tutor study_other study_affect_covid hometutoring onlineclass studymyself parentsteach if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_study = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:N} {...}
{c |}{...}
 {res}      145       145       145       145       145       145       145
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
          tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
N {res}          145           145           145           145           145           145           145
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:N} {...}
{c |}{...}
 {res}       98        98        98        98        98        98        98
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
          tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
N {res}           98            98            98            98            98            98            98
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:Mean} {...}
{c |}{...}
 {res}  .337931   .462069  .6482759  .0965517  .0482759  .5241379  .0275862
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
             tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
Mean {res}    .33793103     .46206897     .64827586     .09655172     .04827586     .52413793     .02758621
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .4591837  .4285714  .6020408  .1428571  .1326531  .5306122  .1938776
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
             tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
Mean {res}    .45918367     .42857143     .60204082     .14285714     .13265306     .53061224     .19387755
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:SD} {...}
{c |}{...}
 {res} .4746445  .5002873  .4791635  .2963701  .2150915  .5011481  .1643517
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
           tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
SD {res}    .47464445     .50028727     .47916354     .29637012     .21509153     .50114811     .16435174
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:SD} {...}
{c |}{...}
 {res} .5008934   .497416  .4919935  .3517262  .3409434  .5016279  .3973667
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
           tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
SD {res}    .50089337       .497416     .49199354     .35172623     .34094336      .5016279     .39736667
{reset}
{com}. 
. matrix n_study = J(1,8,.)
{txt}
{com}. forvalues i = 1/8 {c -(}
{txt}  2{com}.         matrix n_study[1,`i'] = n_tr_study[1,`i'] + n_ct_study[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in tutor study_other study_affect_covid hometutoring onlineclass studymyself parentsteach{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                   tutor{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.1212526{col 38}{space 1}  -1.69{col 46}{space 3}0.112{col 54}{space 3}-.2755537{col 66}{space 3} .0332078
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}             study_other{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0334975{col 38}{space 1}   0.39{col 46}{space 3}0.692{col 54}{space 3}-.1600984{col 66}{space 3} .2282668
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}      study_affect_covid{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  .046235{col 38}{space 1}   0.56{col 46}{space 3}0.604{col 54}{space 3}-.1180318{col 66}{space 3}  .211968
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}            hometutoring{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0463054{col 38}{space 1}  -1.11{col 46}{space 3}0.332{col 54}{space 3}-.1291883{col 66}{space 3} .0531965
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}             onlineclass{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0843772{col 38}{space 1}  -1.92{col 46}{space 3}0.088{col 54}{space 3}-.1768716{col 66}{space 3} .0126979
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:19})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}             studymyself{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0064743{col 38}{space 1}  -0.08{col 46}{space 3}0.956{col 54}{space 3}-.1589465{col 66}{space 3} .1656568
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}            parentsteach{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.1662913{col 38}{space 1}  -3.85{col 46}{space 3}0.000{col 54}{space 3}-.2590465{col 66}{space 3}-.0621506
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. /// Cognitive
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat followup_cog_std if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_cog = r(StatTotal)
{txt}  5{com}. 
. tabstat followup_cog_std if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_cog = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}{ralign 12:Variable} {...}
{c |}         N
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res}      145
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
   followup_c~d
N {res}          145
{reset}
{ralign 12:Variable} {...}
{c |}         N
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res}       98
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
   followup_c~d
N {res}           98
{reset}
{ralign 12:Variable} {...}
{c |}      Mean
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res}-.0920409
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
      followup_c~d
Mean {res}   -.09204085
{reset}
{ralign 12:Variable} {...}
{c |}      Mean
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res} .1361831
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
      followup_c~d
Mean {res}    .13618309
{reset}
{ralign 12:Variable} {...}
{c |}        SD
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res} 1.070796
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
    followup_c~d
SD {res}     1.070796
{reset}
{ralign 12:Variable} {...}
{c |}        SD
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res} .8725076
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
    followup_c~d
SD {res}    .87250763
{reset}
{com}. 
. matrix n_cog = J(1,1,.)
{txt}
{com}. forvalues i = 1/1 {c -(}
{txt}  2{com}.         matrix n_cog[1,`i'] = n_tr_cog[1,`i'] + n_ct_cog[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2282239{col 38}{space 1}  -1.36{col 46}{space 3}0.188{col 54}{space 3}-.5844915{col 66}{space 3} .1076236
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}.         
. matrix r2_followup_cog_std_temp = r(table)
{txt}
{com}. 
. 
. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix r2_followup_cog_std_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix r2_followup_cog_std_mean[1,`j'] = r2_followup_cog_std_temp[1,`j']
{txt}  3{com}. * standard error
. * matrix r2_followup_cog_std_se[1,`j'] = r2_followup_cog_std_temp[2,`j']
. * p value
. matrix r2_followup_cog_std_pv[1,`j'] = r2_followup_cog_std_temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}.     
. /// Non cognitive
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat followup_noncog_std RSES_std CPCS_std if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_noncog = r(StatTotal)
{txt}  5{com}. 
. tabstat followup_noncog_std RSES_std CPCS_std if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_noncog = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}      105       140       140
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   followup_n~d      RSES_std      CPCS_std
N {res}          105           140           140
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}       74        96        96
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   followup_n~d      RSES_std      CPCS_std
N {res}           74            96            96
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .1969319  .1591241  .1745941
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      followup_n~d      RSES_std      CPCS_std
Mean {res}    .19693189      .1591241     .17459415
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res}-.2794302 -.2320565  -.254617
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      followup_n~d      RSES_std      CPCS_std
Mean {res}   -.27943024    -.23205648    -.25461705
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} 1.006158  1.022691  1.008304
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    followup_n~d      RSES_std      CPCS_std
SD {res}    1.0061577     1.0226907     1.0083041
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} .9279901  .9228443  .9357831
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    followup_n~d      RSES_std      CPCS_std
SD {res}    .92799012     .92284427     .93578307
{reset}
{com}. 
. matrix n_noncog = J(1,3,.)
{txt}
{com}. forvalues i = 1/3 {c -(}
{txt}  2{com}.         matrix n_noncog[1,`i'] = n_tr_noncog[1,`i'] + n_ct_noncog[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in followup_noncog_std RSES_std CPCS_std{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}179
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 32
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}5.6
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}     followup_noncog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .4763621{col 38}{space 1}   2.08{col 46}{space 3}0.086{col 54}{space 3}-.0637053{col 66}{space 3} .9908392
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:29})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}236
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.2
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3911806{col 38}{space 1}   2.02{col 46}{space 3}0.084{col 54}{space 3}-.0409187{col 66}{space 3} .7724405
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}30{text} done{text} ({result:30})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}236
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.2
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .4292112{col 38}{space 1}   2.26{col 46}{space 3}0.038{col 54}{space 3} .0220722{col 66}{space 3} .8573663
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. 
. /// Behavioral
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat hyper if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_hyper = r(StatTotal)
{txt}  5{com}. 
. tabstat hyper if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_hyper = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}{ralign 12:Variable} {...}
{c |}         N
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res}      113
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
   hyper
N {res}   113
{reset}
{ralign 12:Variable} {...}
{c |}         N
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res}       71
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
   hyper
N {res}    71
{reset}
{ralign 12:Variable} {...}
{c |}      Mean
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res} .2654867
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
          hyper
Mean {res} .26548673
{reset}
{ralign 12:Variable} {...}
{c |}      Mean
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res} .0704225
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
          hyper
Mean {res} .07042254
{reset}
{ralign 12:Variable} {...}
{c |}        SD
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res}  .443559
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
        hyper
SD {res} .44355905
{reset}
{ralign 12:Variable} {...}
{c |}        SD
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res} .2576789
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
        hyper
SD {res} .25767885
{reset}
{com}. 
. matrix n_hyper = J(1,1,.)
{txt}
{com}. forvalues i = 1/1 {c -(}
{txt}  2{com}.         matrix n_hyper[1,`i'] = n_tr_hyper[1,`i'] + n_ct_hyper[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in hyper{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment if hypernoinfo == 0, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}184
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}5.6
{col 69}{txt}max{col 72} = {res} 12
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                   hyper{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1950642{col 38}{space 1}   3.37{col 46}{space 3}0.006{col 54}{space 3}  .077162{col 66}{space 3} .3289795
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. 
. // significant level
. 
. local outcome DT_score_pre_std rosen_pre_std cpcs_pre_std hhmember hhheadage hhheadeduyear q2a q2c q2h tutor study_other followup_cog_std RSES_std CPCS_std
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}.                 if r2_`dep'_pv[1,1]<=0.01 {c -(}
{txt}  3{com}.                         local star_`dep' %3s "***"
{txt}  4{com}.                 {c )-}
{txt}  5{com}.                 else if (r2_`dep'_pv[1,1]>0.01) & (r2_`dep'_pv[1,1]<=0.05) {c -(}
{txt}  6{com}.                         local star_`dep' %2s "**"
{txt}  7{com}.                 {c )-}
{txt}  8{com}.                 else if (r2_`dep'_pv[1,1]>0.05) & (r2_`dep'_pv[1,1]<=0.10) {c -(}
{txt}  9{com}.                         local star_`dep' %1s "*"
{txt} 10{com}.                 {c )-}
{txt} 11{com}.                 else {c -(}
{txt} 12{com}.                         local star_`dep'  ""
{txt} 13{com}.                 {c )-}
{txt} 14{com}. {c )-} 
{txt}
{com}. 
. rwolf DT_score_pre_std rosen_pre_std cpcs_pre_std hhmember hhheadage hhheadeduyear, indepvar(treatment) reps(1000)
Bootstrap replications (1000). This may take some time.
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Romano-Wolf step-down adjusted p-values


Independent variable:  treatment
Outcome variables:   DT_score_pre_std rosen_pre_std cpcs_pre_std hhmember
{col 22}hhheadage hhheadeduyear
Number of resamples: 1000


{hline 78}
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
{hline 20}+{hline 57}
   {txt}DT_score_pre_std {c |}    {res}0.5518             0.5544              0.8472
      {txt}rosen_pre_std {c |}    {res}0.4689             0.4605              0.8472
       {txt}cpcs_pre_std {c |}    {res}0.0105             0.0150              0.0460
           {txt}hhmember {c |}    {res}0.1345             0.1349              0.4336
          {txt}hhheadage {c |}    {res}0.9229             0.9191              0.9191
      {txt}hhheadeduyear {c |}    {res}0.0494             0.0619              0.2388
{hline 78}
{txt}
{com}. rwolf q2a q2c q2h tutor study_other followup_cog_std RSES_std CPCS_std, indepvar(treatment) reps(1000)
Bootstrap replications (1000). This may take some time.
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Romano-Wolf step-down adjusted p-values


Independent variable:  treatment
Outcome variables:   q2a q2c q2h tutor study_other followup_cog_std RSES_std CPCS_std
Number of resamples: 1000


{hline 78}
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
{hline 20}+{hline 57}
                {txt}q2a {c |}    {res}0.7471             0.7323              0.7822
                {txt}q2c {c |}    {res}0.4722             0.4466              0.7822
                {txt}q2h {c |}    {res}0.2801             0.2747              0.5804
              {txt}tutor {c |}    {res}0.0573             0.0709              0.2498
        {txt}study_other {c |}    {res}0.6083             0.6094              0.7822
   {txt}followup_cog_std {c |}    {res}0.0809             0.0889              0.2717
           {txt}RSES_std {c |}    {res}0.0030             0.0040              0.0190
           {txt}CPCS_std {c |}    {res}0.0011             0.0020              0.0080
{hline 78}
{txt}
{com}. 
. 
. /// Table
> tempname hh2
{txt}
{com}. file open `hh2' using "$path_output/summary_stat.tex", write replace
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "% Author: Kazuma Takakura" _newline
{txt}
{com}. file write `hh2' "% Date: `c(current_date)'" _newline
{txt}
{com}. file write `hh2' "% Time: `c(current_time)'" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. 
. file write `hh2' "\begin{c -(}table{c )-}[h!]\footnotesize" _newline
{txt}
{com}. file write `hh2' "  \centering" _newline
{txt}
{com}. file write `hh2' "  \caption{c -(}Summary Statistics{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:sumstat{c )-}" _newline
{txt}
{com}. file write `hh2' "\scalebox{c -(}1{c )-}{c -(}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}threeparttable{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "\begin{c -(}tabular{c )-}{c -(}lccccc{c )-}\toprule" _newline
{txt}
{com}. 
.   
. file write `hh2' " Dependent Variable & Treatment &  Control  & Difference & N   \\\midrule\midrule" _newline
{txt}
{com}. file write `hh2' " Panel A: Baseline & & & &   \\ " _newline
{txt}
{com}. file write `hh2' " DT score^{c -(}a{c )-} & " %04.3f (mean_tr_bl[1,1]) " & " %04.3f (mean_ct_bl[1,1]) " & " %04.3f (r2_DT_score_pre_std_mean[1,1]) `star_DT_score_pre_std' " & " (n_bl[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_bl[1,1]) " ] & [ " %04.3f (sd_ct_bl[1,1]) " ] & ( " %04.3f (r2_DT_score_pre_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.831) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. 
. file write `hh2' " RSES score^{c -(}a{c )-} & " %04.3f (mean_tr_bl[1,2]) " & " %04.3f (mean_ct_bl[1,2]) " & " %04.3f (r2_rosen_pre_std_mean[1,1]) `star_rosen_pre_std' " & "  (n_bl[1,2]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_bl[1,2]) " ] & [ " %04.3f (sd_ct_bl[1,2]) " ] & ( " %04.3f (r2_rosen_pre_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.831) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " CPCS score^{c -(}a{c )-} & " %04.3f (mean_tr_bl[1,3]) " & " %04.3f (mean_ct_bl[1,3]) " & " %04.3f (r2_cpcs_pre_std_mean[1,1]) `star_cpcs_pre_std' " & "  (n_bl[1,3]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_bl[1,3]) " ] & [ " %04.3f (sd_ct_bl[1,3]) " ] & ( " %04.3f (r2_cpcs_pre_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.059) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Household size & " %04.3f (mean_tr_parent[1,1]) " & " %04.3f (mean_ct_parent[1,1]) " & " %04.3f (r2_hhmember_mean[1,1]) `star_hhmember'  " & " (n_parent[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_parent[1,1]) " ] & [ " %04.3f (sd_ct_parent[1,1]) " ] & ( " %04.3f (r2_hhmember_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.464) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Household head age & " %04.3f (mean_tr_parent[1,2]) " & " %04.3f (mean_ct_parent[1,2]) " & " %04.3f (r2_hhheadage_mean[1,1]) `star_hhheadage' " & "  (n_parent[1,2]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_parent[1,2]) " ] & [ " %04.3f (sd_ct_parent[1,2]) " ] & ( " %04.3f (r2_hhheadage_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.920) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Household head education & " %04.3f (mean_tr_parent[1,3]) " & " %04.3f (mean_ct_parent[1,3]) " & " %04.3f (r2_hhheadeduyear_mean[1,1]) `star_hhheadeduyear' " & "  (n_parent[1,3]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_parent[1,3]) " ] & [ " %04.3f (sd_ct_parent[1,3]) " ] & ( " %04.3f (r2_hhheadeduyear_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.248) " \{c )-} &   \\ " _newline
{txt}
{com}. file write `hh2' " \\ "_newline
{txt}
{com}. 
. file write `hh2' " Panel B: Follow-up & & & &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " School attendance & " %04.3f (mean_tr_school[1,1]) " & " %04.3f (mean_ct_school[1,1]) " & " %04.3f (r2_q2a_mean[1,1]) `star_q2a' " & " (n_school[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_school[1,1]) " ] & [ " %04.3f (sd_ct_school[1,1]) " ] & ( " %04.3f (r2_q2a_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.787) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Grade repeat & " %04.3f (mean_tr_school[1,3]) " & " %04.3f (mean_ct_school[1,3]) " & " %04.3f (r2_q2c_mean[1,1]) `star_q2c' " & "  (n_school[1,3]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_school[1,3]) " ] & [ " %04.3f (sd_ct_school[1,3]) " ] & ( " %04.3f (r2_q2c_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.787) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Drop out & " %04.3f (mean_tr_school[1,4]) " & " %04.3f (mean_ct_school[1,4]) " & " %04.3f (r2_q2h_mean[1,1]) `star_q2h'  " & "  (n_school[1,4]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_school[1,4]) " ] & [ " %04.3f (sd_ct_school[1,4]) " ] & ( " %04.3f (r2_q2h_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.576) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Tutoring & " %04.3f (mean_tr_study[1,1]) " & " %04.3f (mean_ct_study[1,1]) " & " %04.3f (r2_tutor_mean[1,1]) `star_tutor'  " & " (n_study[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_study[1,1]) " ] & [ " %04.3f (sd_ct_study[1,1]) " ] & ( " %04.3f (r2_tutor_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.230) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Self-study & " %04.3f (mean_tr_study[1,2]) " & " %04.3f (mean_ct_study[1,2]) " & " %04.3f (r2_study_other_mean[1,1]) `star_study_other' " & "  (n_study[1,2]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_study[1,2]) " ] & [ " %04.3f (sd_ct_study[1,2]) " ] & ( " %04.3f (r2_study_other_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.787) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Rapid math test score^{c -(}a{c )-} & " %04.3f (mean_tr_cog[1,1]) " & " %04.3f (mean_ct_cog[1,1]) " & " %04.3f (r2_followup_cog_std_mean[1,1]) `star_followup_cog_std'  "  & "  (n_cog[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_cog[1,1]) " ] & [ " %04.3f (sd_ct_cog[1,1]) " ] & ( " %04.3f (r2_followup_cog_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.270) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " RSES score^{c -(}a{c )-} & " %04.3f (mean_tr_noncog[1,2]) " & " %04.3f (mean_ct_noncog[1,2]) " & " %04.3f (r2_RSES_std_mean[1,1])   `star_RSES_std' " & " (n_noncog[1,2]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_noncog[1,2]) " ] & [ " %04.3f (sd_ct_noncog[1,2]) " ] & ( " %04.3f (r2_RSES_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.011) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " CPCS score^{c -(}a{c )-} & " %04.3f (mean_tr_noncog[1,3]) " & " %04.3f (mean_ct_noncog[1,3]) " & " %04.3f (r2_CPCS_std_mean[1,1])   `star_CPCS_std' "&  " (n_noncog[1,3]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_noncog[1,3]) " ] & [ " %04.3f (sd_ct_noncog[1,3]) " ] & ( " %04.3f (r2_CPCS_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.006) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. 
. file write `hh2' "\midrule" _newline
{txt}
{com}. file write `hh2' "\end{c -(}tabular{c )-}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\item (a) Variables are standardized using the average and variance of the whole follow-up sample in the February 2022 survey. " _newline
{txt}
{com}. file write `hh2' "\item (b) Standard deviations are reported in square brackets.  " _newline
{txt}
{com}. file write `hh2' "\item (c) Wild clustered bootstrap p-values are reported within parentheses. Clusters are schools at the baseline. There are 34 clusters. " _newline
{txt}
{com}. file write `hh2' "\item (d) Romano-Wolf multiple hypothesis testing p-values are reported in curly brackets. This test is conducted separately for the baseline variables and the follow-up variables." _newline
{txt}
{com}. file write `hh2' "\item (e) Statistical significance is indicated by stars based on the wild clustered bootstrap p-values reported in parentheses: $*$ denotes significance at the 10\% level, $∗∗$ at the 5\% level, and $∗∗∗$ at the 1\% level.  " _newline
{txt}
{com}. file write `hh2' "\end{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}threeparttable{c )-}" _newline
{txt}
{com}. file write `hh2' "{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}table{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. file close `hh2'
{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/mb/s9qljd594gbfyhl2dqnnwlcw0000gn/T//SD56186.000000"
{txt}
{com}. * This is the do file to create "Table 5. Heterogeneity by Baseline Abilites (Math and CPCS)"
. set seed 1
{txt}
{com}. 
. use "$path_data/temp/followup_student_baseline_score_missing_dummy", replace
{txt}
{com}. 
. egen DT_score_pre_std_med = median(DT_score_pre_std)
{txt}
{com}. gen DT_score_pre_std_upper50 = 1 if DT_score_pre_std>DT_score_pre_std_med
{txt}(121 missing values generated)

{com}. recode DT_score_pre_std_upper50 (.=0)
{txt}(121 changes made to {bf:DT_score_pre_std_upper50})

{com}. 
. egen rosen_pre_std_med = median(rosen_pre_std)
{txt}
{com}. gen rosen_pre_std_upper50 = 1 if rosen_pre_std>rosen_pre_std_med
{txt}(128 missing values generated)

{com}. recode rosen_pre_std_upper50 (.=0)
{txt}(128 changes made to {bf:rosen_pre_std_upper50})

{com}. rename rosen_pre_std_upper50 RSES_std_upper50
{res}{txt}
{com}. 
. egen cpcs_pre_std_med = median(cpcs_pre_std)
{txt}
{com}. gen cpcs_pre_std_upper50 = 1 if cpcs_pre_std>cpcs_pre_std_med
{txt}(135 missing values generated)

{com}. recode cpcs_pre_std_upper50 (.=0)
{txt}(135 changes made to {bf:cpcs_pre_std_upper50})

{com}. rename cpcs_pre_std_upper50 CPCS_std_upper50
{res}{txt}
{com}. 
. 
. /// Cognitive
> wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 59
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 22
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2397567{col 38}{space 1}  -0.86{col 46}{space 3}0.412{col 54}{space 3}-.8323853{col 66}{space 3} .4175037
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_rses_u = e(N)
{txt}
{com}. scalar n_clust_cog_u_rses_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.3459862{col 38}{space 1}  -1.14{col 46}{space 3}0.252{col 54}{space 3}-.9957895{col 66}{space 3} .2894481
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_rses_l = e(N)
{txt}
{com}. scalar n_clust_cog_u_rses_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2419353{col 38}{space 1}  -0.87{col 46}{space 3}0.414{col 54}{space 3}-.7989934{col 66}{space 3} .4263604
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_rses_u = e(N)
{txt}
{com}. scalar n_clust_cog_l_rses_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0781293{col 38}{space 1}  -0.29{col 46}{space 3}0.798{col 54}{space 3}-.6024584{col 66}{space 3} .5774076
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_rses_l = e(N)
{txt}
{com}. scalar n_clust_cog_l_rses_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 55
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0533319{col 38}{space 1}   0.17{col 46}{space 3}0.876{col 54}{space 3}-.6050722{col 66}{space 3} .7471398
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_cpcs_u = e(N)
{txt}
{com}. scalar n_clust_cog_u_cpcs_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 67
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.5660393{col 38}{space 1}  -1.84{col 46}{space 3}0.058{col 54}{space 3}-1.200423{col 66}{space 3} .0265449
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_cpcs_l = e(N)
{txt}
{com}. scalar n_clust_cog_u_cpcs_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0295186{col 38}{space 1}   0.10{col 46}{space 3}0.890{col 54}{space 3}-.6279539{col 66}{space 3} .7132061
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_cpcs_u = e(N)
{txt}
{com}. scalar n_clust_cog_l_cpcs_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 68
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  -.19243{col 38}{space 1}  -0.73{col 46}{space 3}0.504{col 54}{space 3}-.7344232{col 66}{space 3} .3561501
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_cpcs_l = e(N)
{txt}
{com}. scalar n_clust_cog_l_cpcs_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. 
. 
. /// RSES
> wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:19})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .9579135{col 38}{space 1}   3.36{col 46}{space 3}0.012{col 54}{space 3} .2718992{col 66}{space 3} 1.597382
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_u_cog_u = e(N)
{txt}
{com}. scalar n_clust_rses_u_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 61
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0615952{col 38}{space 1}   0.32{col 46}{space 3}0.804{col 54}{space 3} -.346128{col 66}{space 3} .4345873
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_l_cog_u = e(N)
{txt}
{com}. scalar n_clust_rses_l_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .4920182{col 38}{space 1}   1.44{col 46}{space 3}0.182{col 54}{space 3}-.3275928{col 66}{space 3} 1.187104
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_u_cog_l = e(N)
{txt}
{com}. scalar n_clust_rses_u_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1388312{col 38}{space 1}   0.42{col 46}{space 3}0.674{col 54}{space 3}-.6025065{col 66}{space 3} .9137282
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_l_cog_l = e(N)
{txt}
{com}. scalar n_clust_rses_l_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 52
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 20
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .6664341{col 38}{space 1}   3.26{col 46}{space 3}0.010{col 54}{space 3}  .223697{col 66}{space 3} 1.128999
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3847293{col 38}{space 1}   1.53{col 46}{space 3}0.150{col 54}{space 3}-.1528505{col 66}{space 3} .9279308
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2062219{col 38}{space 1}   0.47{col 46}{space 3}0.642{col 54}{space 3}-.8851328{col 66}{space 3}  1.11006
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 66
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3332931{col 38}{space 1}   0.96{col 46}{space 3}0.388{col 54}{space 3}-.4439605{col 66}{space 3} 1.111003
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. 
. 
. /// CPCS
> 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:19})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} 1.020132{col 38}{space 1}   3.66{col 46}{space 3}0.006{col 54}{space 3} .3435691{col 66}{space 3} 1.713353
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 61
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0665639{col 38}{space 1}   0.34{col 46}{space 3}0.720{col 54}{space 3}-.3345648{col 66}{space 3} .4862635
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .5458399{col 38}{space 1}   1.63{col 46}{space 3}0.134{col 54}{space 3}-.2092327{col 66}{space 3} 1.233199
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1431476{col 38}{space 1}   0.41{col 46}{space 3}0.696{col 54}{space 3}-.6640145{col 66}{space 3} .9784031
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 52
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 20
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  .774167{col 38}{space 1}   3.68{col 46}{space 3}0.002{col 54}{space 3} .3112148{col 66}{space 3} 1.305168
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_u_cog_u = e(N)
{txt}
{com}. scalar n_clust_cpcs_u_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3575321{col 38}{space 1}   1.51{col 46}{space 3}0.144{col 54}{space 3}-.1375077{col 66}{space 3} .8888439
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_l_cog_u = e(N)
{txt}
{com}. scalar n_clust_cpcs_l_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2792052{col 38}{space 1}   0.63{col 46}{space 3}0.524{col 54}{space 3}-.8592776{col 66}{space 3} 1.292293
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_u_cog_l = e(N)
{txt}
{com}. scalar n_clust_cpcs_u_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 66
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}   .33148{col 38}{space 1}   0.95{col 46}{space 3}0.384{col 54}{space 3}-.4816208{col 66}{space 3} 1.038965
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_l_cog_l = e(N)
{txt}
{com}. scalar n_clust_cpcs_l_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. // significant level
. 
. 
. local outcome cog rses cpcs
{txt}
{com}. local hetero1 cogU cogL
{txt}
{com}. local hetero2 rsesU rsesL
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. foreach h1 in `hetero1'{c -(}
{txt}  3{com}. foreach h2 in `hetero2'{c -(}
{txt}  4{com}.                 if `dep'_`h1'_`h2'_pv[1,1]<=0.01 {c -(}
{txt}  5{com}.                         local star_`dep'_`h1'_`h2' %3s "***"
{txt}  6{com}.                 {c )-}
{txt}  7{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.01) & (`dep'_`h1'_`h2'_pv[1,1]<=0.05) {c -(}
{txt}  8{com}.                         local star_`dep'_`h1'_`h2' %2s "**"
{txt}  9{com}.                 {c )-}
{txt} 10{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.05) & (`dep'_`h1'_`h2'_pv[1,1]<=0.10) {c -(}
{txt} 11{com}.                         local star_`dep'_`h1'_`h2' %1s "*"
{txt} 12{com}.                 {c )-}
{txt} 13{com}.                 else {c -(}
{txt} 14{com}.                         local star_`dep'_`h1'_`h2'  ""
{txt} 15{com}.                 {c )-}
{txt} 16{com}. {c )-} 
{txt} 17{com}. {c )-}
{txt} 18{com}. {c )-}
{txt}
{com}. 
. local outcome cog rses cpcs
{txt}
{com}. local hetero1 cogU cogL
{txt}
{com}. local hetero2 cpcsU cpcsL
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. foreach h1 in `hetero1'{c -(}
{txt}  3{com}. foreach h2 in `hetero2'{c -(}
{txt}  4{com}.                 if `dep'_`h1'_`h2'_pv[1,1]<=0.01 {c -(}
{txt}  5{com}.                         local star_`dep'_`h1'_`h2' %3s "***"
{txt}  6{com}.                 {c )-}
{txt}  7{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.01) & (`dep'_`h1'_`h2'_pv[1,1]<=0.05) {c -(}
{txt}  8{com}.                         local star_`dep'_`h1'_`h2' %2s "**"
{txt}  9{com}.                 {c )-}
{txt} 10{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.05) & (`dep'_`h1'_`h2'_pv[1,1]<=0.10) {c -(}
{txt} 11{com}.                         local star_`dep'_`h1'_`h2' %1s "*"
{txt} 12{com}.                 {c )-}
{txt} 13{com}.                 else {c -(}
{txt} 14{com}.                         local star_`dep'_`h1'_`h2'  ""
{txt} 15{com}.                 {c )-}
{txt} 16{com}. {c )-} 
{txt} 17{com}. {c )-}
{txt} 18{com}. {c )-}
{txt}
{com}. 
. /// Table
> 
. tempname hh2
{txt}
{com}. file open `hh2' using "$path_output/hetero_2by2_CPCS.tex", write replace
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "% Author: Kazuma Takakura" _newline
{txt}
{com}. file write `hh2' "% Date: `c(current_date)'" _newline
{txt}
{com}. file write `hh2' "% Time: `c(current_time)'" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. 
. file write `hh2' "\begin{c -(}table{c )-}[h!]\footnotesize" _newline
{txt}
{com}. file write `hh2' "  \centering" _newline
{txt}
{com}. file write `hh2' "  \caption{c -(}Heterogeneity among Baseline Abilites (Math and CPCS){c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:hetero_cpcs{c )-}" _newline
{txt}
{com}. file write `hh2' "\scalebox{c -(}1{c )-}{c -(}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}threeparttable{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "\begin{c -(}tabular{c )-}{c -(}lccccccc{c )-}\toprule" _newline
{txt}
{com}. 
. file write `hh2' " Dependent Variable & \multicolumn{c -(}2{c )-}{c -(}c{c )-}{c -(}Baseline^{c -(}b{c )-}{c )-} & Difference & Obs & N of clusters  \\\midrule\midrule" _newline
{txt}
{com}. file write `hh2' " Rapid math test score^{c -(}a{c )-} & Math Top 50\%  & CPCS Top 50\% & " %04.3f (cog_cogU_rsesU_mean[1,1]) `star_cog_cogU_cpcsU' " & " %02.0f ( n_cog_u_cpcs_u ) " & " %02.0f ( n_clust_cog_u_cpcs_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogU_cpcsU_pv[1,1]) " )  & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cog_cogU_cpcsL_mean[1,1]) `star_cog_cogU_cpcsL' " & " %02.0f ( n_cog_u_cpcs_l ) " & " %02.0f ( n_clust_cog_u_cpcs_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogU_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & Math Bottom 50\%  & CPCS Top 50\% & " %04.3f (cog_cogL_cpcsU_mean[1,1]) `star_cog_cogL_cpcsU' " & " %02.0f ( n_cog_l_cpcs_u ) " & " %02.0f ( n_clust_cog_l_cpcs_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogL_cpcsU_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cog_cogL_cpcsL_mean[1,1]) `star_cog_cogL_cpcsL' " & " %02.0f ( n_cog_l_cpcs_l ) " & " %02.0f ( n_clust_cog_l_cpcs_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogL_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. 
. file write `hh2' " CPCS score^{c -(}a{c )-} & Math Top 50\%  & CPCS Top 50\% & " %04.3f (cpcs_cogU_cpcsU_mean[1,1]) `star_cpcs_cogU_cpcsU' " & " %02.0f ( n_cpcs_u_cog_u ) " & " %02.0f ( n_clust_cpcs_u_cog_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cpcs_cogU_cpcsU_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cpcs_cogU_cpcsL_mean[1,1]) `star_cpcs_cogU_cpcsL' " & " %02.0f ( n_cpcs_l_cog_u ) " & " %02.0f ( n_clust_cpcs_l_cog_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &       & & ( " %04.3f (cpcs_cogU_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & Math Bottom 50\%  & CPCS Top 50\% & " %04.3f (cpcs_cogL_cpcsU_mean[1,1]) `star_cpcs_cogL_cpcsU' " & " %02.0f ( n_cpcs_u_cog_l ) " & " %02.0f ( n_clust_cpcs_u_cog_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cpcs_cogL_cpcsU_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cpcs_cogL_cpcsL_mean[1,1]) `star_cpcs_cogL_cpcsL' " & " %02.0f ( n_cpcs_l_cog_l ) " & " %02.0f ( n_clust_cpcs_l_cog_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &       & & ( " %04.3f (cpcs_cogL_cpcsL_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. 
. file write `hh2' "\\\midrule" _newline
{txt}
{com}. file write `hh2' "\end{c -(}tabular{c )-}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\item (a) Dependent variables are standardized using the average and variance of the whole follow-up sample in the February 2022 survey. " _newline
{txt}
{com}. file write `hh2' "\item (b) Cutoffs are created based on whether their ability to perform each item at the baseline was higher or lower than the median. " _newline
{txt}
{com}. file write `hh2' "\item (c) Wild cluster bootstrap p-values are reported within parentheses. Clusters are schools at the baseline." _newline
{txt}
{com}. file write `hh2' "\item (d) $^*$ Significant at 10\% level; $^{c -(}**{c )-}$ significant at 5\% level; $^{c -(}***{c )-}$ significant at 1\% level. " _newline
{txt}
{com}. file write `hh2' "\end{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}threeparttable{c )-}" _newline
{txt}
{com}. file write `hh2' "{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:addlabel{c )-}%" _newline
{txt}
{com}. file write `hh2' "\end{c -(}table{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. file close `hh2'
{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/mb/s9qljd594gbfyhl2dqnnwlcw0000gn/T//SD56186.000000"
{txt}
{com}. * This is the do file to create "Table 5. Heterogeneity by Baseline Abilites (Math and CPCS)"
. set seed 123
{txt}
{com}. 
. use "$path_data/temp/followup_student_baseline_score_missing_dummy", replace
{txt}
{com}. 
. egen DT_score_pre_std_med = median(DT_score_pre_std)
{txt}
{com}. gen DT_score_pre_std_upper50 = 1 if DT_score_pre_std>DT_score_pre_std_med
{txt}(121 missing values generated)

{com}. recode DT_score_pre_std_upper50 (.=0)
{txt}(121 changes made to {bf:DT_score_pre_std_upper50})

{com}. 
. egen rosen_pre_std_med = median(rosen_pre_std)
{txt}
{com}. gen rosen_pre_std_upper50 = 1 if rosen_pre_std>rosen_pre_std_med
{txt}(128 missing values generated)

{com}. recode rosen_pre_std_upper50 (.=0)
{txt}(128 changes made to {bf:rosen_pre_std_upper50})

{com}. rename rosen_pre_std_upper50 RSES_std_upper50
{res}{txt}
{com}. 
. egen cpcs_pre_std_med = median(cpcs_pre_std)
{txt}
{com}. gen cpcs_pre_std_upper50 = 1 if cpcs_pre_std>cpcs_pre_std_med
{txt}(135 missing values generated)

{com}. recode cpcs_pre_std_upper50 (.=0)
{txt}(135 changes made to {bf:cpcs_pre_std_upper50})

{com}. rename cpcs_pre_std_upper50 CPCS_std_upper50
{res}{txt}
{com}. 
. 
. /// Cognitive
> wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 59
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 22
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2397567{col 38}{space 1}  -0.86{col 46}{space 3}0.444{col 54}{space 3} -.835023{col 66}{space 3} .4309843
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_rses_u = e(N)
{txt}
{com}. scalar n_clust_cog_u_rses_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.3459862{col 38}{space 1}  -1.14{col 46}{space 3}0.246{col 54}{space 3}-.9646946{col 66}{space 3} .2967857
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_rses_l = e(N)
{txt}
{com}. scalar n_clust_cog_u_rses_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2419353{col 38}{space 1}  -0.87{col 46}{space 3}0.390{col 54}{space 3}-.8433267{col 66}{space 3} .3920268
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_rses_u = e(N)
{txt}
{com}. scalar n_clust_cog_l_rses_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0781293{col 38}{space 1}  -0.29{col 46}{space 3}0.794{col 54}{space 3}-.6038382{col 66}{space 3} .5443816
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_rses_l = e(N)
{txt}
{com}. scalar n_clust_cog_l_rses_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 55
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0533319{col 38}{space 1}   0.17{col 46}{space 3}0.872{col 54}{space 3}-.6242397{col 66}{space 3} .7907538
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_cpcs_u = e(N)
{txt}
{com}. scalar n_clust_cog_u_cpcs_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 67
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.5660393{col 38}{space 1}  -1.84{col 46}{space 3}0.092{col 54}{space 3}-1.245036{col 66}{space 3} .1419984
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_cpcs_l = e(N)
{txt}
{com}. scalar n_clust_cog_u_cpcs_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0295186{col 38}{space 1}   0.10{col 46}{space 3}0.942{col 54}{space 3}-.6165229{col 66}{space 3} .6514795
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_cpcs_u = e(N)
{txt}
{com}. scalar n_clust_cog_l_cpcs_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 68
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  -.19243{col 38}{space 1}  -0.73{col 46}{space 3}0.480{col 54}{space 3}-.7462179{col 66}{space 3} .3675341
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_cpcs_l = e(N)
{txt}
{com}. scalar n_clust_cog_l_cpcs_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. 
. 
. /// RSES
> wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .9579135{col 38}{space 1}   3.36{col 46}{space 3}0.000{col 54}{space 3} .3158411{col 66}{space 3} 1.651529
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_u_cog_u = e(N)
{txt}
{com}. scalar n_clust_rses_u_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 61
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0615952{col 38}{space 1}   0.32{col 46}{space 3}0.748{col 54}{space 3} -.335182{col 66}{space 3} .4398791
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_l_cog_u = e(N)
{txt}
{com}. scalar n_clust_rses_l_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:29})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .4920182{col 38}{space 1}   1.44{col 46}{space 3}0.152{col 54}{space 3}-.1956882{col 66}{space 3} 1.132075
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_u_cog_l = e(N)
{txt}
{com}. scalar n_clust_rses_u_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1388312{col 38}{space 1}   0.42{col 46}{space 3}0.748{col 54}{space 3}-.6586927{col 66}{space 3} .8819884
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_l_cog_l = e(N)
{txt}
{com}. scalar n_clust_rses_l_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 52
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 20
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .6664341{col 38}{space 1}   3.26{col 46}{space 3}0.004{col 54}{space 3} .2256924{col 66}{space 3}  1.16988
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3847293{col 38}{space 1}   1.53{col 46}{space 3}0.150{col 54}{space 3}-.1439999{col 66}{space 3} .9723153
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2062219{col 38}{space 1}   0.47{col 46}{space 3}0.630{col 54}{space 3}-.9392839{col 66}{space 3} 1.108811
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 66
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3332931{col 38}{space 1}   0.96{col 46}{space 3}0.416{col 54}{space 3}-.4864367{col 66}{space 3} 1.101715
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. 
. 
. /// CPCS
> 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} 1.020132{col 38}{space 1}   3.66{col 46}{space 3}0.002{col 54}{space 3} .4199485{col 66}{space 3} 1.782028
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 61
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0665639{col 38}{space 1}   0.34{col 46}{space 3}0.706{col 54}{space 3}-.3338298{col 66}{space 3} .4994486
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .5458399{col 38}{space 1}   1.63{col 46}{space 3}0.112{col 54}{space 3}-.2035501{col 66}{space 3} 1.209728
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1431476{col 38}{space 1}   0.41{col 46}{space 3}0.700{col 54}{space 3}-.6565782{col 66}{space 3} 1.032317
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:19})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 52
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 20
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  .774167{col 38}{space 1}   3.68{col 46}{space 3}0.006{col 54}{space 3}  .320576{col 66}{space 3} 1.297191
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_u_cog_u = e(N)
{txt}
{com}. scalar n_clust_cpcs_u_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3575321{col 38}{space 1}   1.51{col 46}{space 3}0.154{col 54}{space 3} -.150128{col 66}{space 3} .9146968
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_l_cog_u = e(N)
{txt}
{com}. scalar n_clust_cpcs_l_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2792052{col 38}{space 1}   0.63{col 46}{space 3}0.546{col 54}{space 3}-.9224898{col 66}{space 3} 1.348458
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_u_cog_l = e(N)
{txt}
{com}. scalar n_clust_cpcs_u_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 66
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}   .33148{col 38}{space 1}   0.95{col 46}{space 3}0.420{col 54}{space 3}-.5052153{col 66}{space 3} 1.053721
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_l_cog_l = e(N)
{txt}
{com}. scalar n_clust_cpcs_l_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. // significant level
. 
. 
. local outcome cog rses cpcs
{txt}
{com}. local hetero1 cogU cogL
{txt}
{com}. local hetero2 rsesU rsesL
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. foreach h1 in `hetero1'{c -(}
{txt}  3{com}. foreach h2 in `hetero2'{c -(}
{txt}  4{com}.                 if `dep'_`h1'_`h2'_pv[1,1]<=0.01 {c -(}
{txt}  5{com}.                         local star_`dep'_`h1'_`h2' %3s "***"
{txt}  6{com}.                 {c )-}
{txt}  7{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.01) & (`dep'_`h1'_`h2'_pv[1,1]<=0.05) {c -(}
{txt}  8{com}.                         local star_`dep'_`h1'_`h2' %2s "**"
{txt}  9{com}.                 {c )-}
{txt} 10{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.05) & (`dep'_`h1'_`h2'_pv[1,1]<=0.10) {c -(}
{txt} 11{com}.                         local star_`dep'_`h1'_`h2' %1s "*"
{txt} 12{com}.                 {c )-}
{txt} 13{com}.                 else {c -(}
{txt} 14{com}.                         local star_`dep'_`h1'_`h2'  ""
{txt} 15{com}.                 {c )-}
{txt} 16{com}. {c )-} 
{txt} 17{com}. {c )-}
{txt} 18{com}. {c )-}
{txt}
{com}. 
. local outcome cog rses cpcs
{txt}
{com}. local hetero1 cogU cogL
{txt}
{com}. local hetero2 cpcsU cpcsL
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. foreach h1 in `hetero1'{c -(}
{txt}  3{com}. foreach h2 in `hetero2'{c -(}
{txt}  4{com}.                 if `dep'_`h1'_`h2'_pv[1,1]<=0.01 {c -(}
{txt}  5{com}.                         local star_`dep'_`h1'_`h2' %3s "***"
{txt}  6{com}.                 {c )-}
{txt}  7{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.01) & (`dep'_`h1'_`h2'_pv[1,1]<=0.05) {c -(}
{txt}  8{com}.                         local star_`dep'_`h1'_`h2' %2s "**"
{txt}  9{com}.                 {c )-}
{txt} 10{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.05) & (`dep'_`h1'_`h2'_pv[1,1]<=0.10) {c -(}
{txt} 11{com}.                         local star_`dep'_`h1'_`h2' %1s "*"
{txt} 12{com}.                 {c )-}
{txt} 13{com}.                 else {c -(}
{txt} 14{com}.                         local star_`dep'_`h1'_`h2'  ""
{txt} 15{com}.                 {c )-}
{txt} 16{com}. {c )-} 
{txt} 17{com}. {c )-}
{txt} 18{com}. {c )-}
{txt}
{com}. 
. /// Table
> 
. tempname hh2
{txt}
{com}. file open `hh2' using "$path_output/hetero_2by2_CPCS.tex", write replace
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "% Author: Kazuma Takakura" _newline
{txt}
{com}. file write `hh2' "% Date: `c(current_date)'" _newline
{txt}
{com}. file write `hh2' "% Time: `c(current_time)'" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. 
. file write `hh2' "\begin{c -(}table{c )-}[h!]\footnotesize" _newline
{txt}
{com}. file write `hh2' "  \centering" _newline
{txt}
{com}. file write `hh2' "  \caption{c -(}Heterogeneity among Baseline Abilites (Math and CPCS){c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:hetero_cpcs{c )-}" _newline
{txt}
{com}. file write `hh2' "\scalebox{c -(}1{c )-}{c -(}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}threeparttable{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "\begin{c -(}tabular{c )-}{c -(}lccccccc{c )-}\toprule" _newline
{txt}
{com}. 
. file write `hh2' " Dependent Variable & \multicolumn{c -(}2{c )-}{c -(}c{c )-}{c -(}Baseline^{c -(}b{c )-}{c )-} & Difference & Obs & N of clusters  \\\midrule\midrule" _newline
{txt}
{com}. file write `hh2' " Rapid math test score^{c -(}a{c )-} & Math Top 50\%  & CPCS Top 50\% & " %04.3f (cog_cogU_rsesU_mean[1,1]) `star_cog_cogU_cpcsU' " & " %02.0f ( n_cog_u_cpcs_u ) " & " %02.0f ( n_clust_cog_u_cpcs_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogU_cpcsU_pv[1,1]) " )  & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cog_cogU_cpcsL_mean[1,1]) `star_cog_cogU_cpcsL' " & " %02.0f ( n_cog_u_cpcs_l ) " & " %02.0f ( n_clust_cog_u_cpcs_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogU_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & Math Bottom 50\%  & CPCS Top 50\% & " %04.3f (cog_cogL_cpcsU_mean[1,1]) `star_cog_cogL_cpcsU' " & " %02.0f ( n_cog_l_cpcs_u ) " & " %02.0f ( n_clust_cog_l_cpcs_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogL_cpcsU_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cog_cogL_cpcsL_mean[1,1]) `star_cog_cogL_cpcsL' " & " %02.0f ( n_cog_l_cpcs_l ) " & " %02.0f ( n_clust_cog_l_cpcs_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogL_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. 
. file write `hh2' " CPCS score^{c -(}a{c )-} & Math Top 50\%  & CPCS Top 50\% & " %04.3f (cpcs_cogU_cpcsU_mean[1,1]) `star_cpcs_cogU_cpcsU' " & " %02.0f ( n_cpcs_u_cog_u ) " & " %02.0f ( n_clust_cpcs_u_cog_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cpcs_cogU_cpcsU_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cpcs_cogU_cpcsL_mean[1,1]) `star_cpcs_cogU_cpcsL' " & " %02.0f ( n_cpcs_l_cog_u ) " & " %02.0f ( n_clust_cpcs_l_cog_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &       & & ( " %04.3f (cpcs_cogU_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & Math Bottom 50\%  & CPCS Top 50\% & " %04.3f (cpcs_cogL_cpcsU_mean[1,1]) `star_cpcs_cogL_cpcsU' " & " %02.0f ( n_cpcs_u_cog_l ) " & " %02.0f ( n_clust_cpcs_u_cog_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cpcs_cogL_cpcsU_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cpcs_cogL_cpcsL_mean[1,1]) `star_cpcs_cogL_cpcsL' " & " %02.0f ( n_cpcs_l_cog_l ) " & " %02.0f ( n_clust_cpcs_l_cog_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &       & & ( " %04.3f (cpcs_cogL_cpcsL_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. 
. file write `hh2' "\\\midrule" _newline
{txt}
{com}. file write `hh2' "\end{c -(}tabular{c )-}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\item (a) Dependent variables are standardized using the average and variance of the whole follow-up sample in the February 2022 survey. " _newline
{txt}
{com}. file write `hh2' "\item (b) Cutoffs are created based on whether their ability to perform each item at the baseline was higher or lower than the median. " _newline
{txt}
{com}. file write `hh2' "\item (c) Wild cluster bootstrap p-values are reported within parentheses. Clusters are schools at the baseline." _newline
{txt}
{com}. file write `hh2' "\item (d) $^*$ Significant at 10\% level; $^{c -(}**{c )-}$ significant at 5\% level; $^{c -(}***{c )-}$ significant at 1\% level. " _newline
{txt}
{com}. file write `hh2' "\end{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}threeparttable{c )-}" _newline
{txt}
{com}. file write `hh2' "{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:addlabel{c )-}%" _newline
{txt}
{com}. file write `hh2' "\end{c -(}table{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. file close `hh2'
{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/mb/s9qljd594gbfyhl2dqnnwlcw0000gn/T//SD56186.000000"
{txt}
{com}. * This is the do file to create "Table 5. Heterogeneity by Baseline Abilites (Math and CPCS)"
. set seed 1234
{txt}
{com}. 
. use "$path_data/temp/followup_student_baseline_score_missing_dummy", replace
{txt}
{com}. 
. egen DT_score_pre_std_med = median(DT_score_pre_std)
{txt}
{com}. gen DT_score_pre_std_upper50 = 1 if DT_score_pre_std>DT_score_pre_std_med
{txt}(121 missing values generated)

{com}. recode DT_score_pre_std_upper50 (.=0)
{txt}(121 changes made to {bf:DT_score_pre_std_upper50})

{com}. 
. egen rosen_pre_std_med = median(rosen_pre_std)
{txt}
{com}. gen rosen_pre_std_upper50 = 1 if rosen_pre_std>rosen_pre_std_med
{txt}(128 missing values generated)

{com}. recode rosen_pre_std_upper50 (.=0)
{txt}(128 changes made to {bf:rosen_pre_std_upper50})

{com}. rename rosen_pre_std_upper50 RSES_std_upper50
{res}{txt}
{com}. 
. egen cpcs_pre_std_med = median(cpcs_pre_std)
{txt}
{com}. gen cpcs_pre_std_upper50 = 1 if cpcs_pre_std>cpcs_pre_std_med
{txt}(135 missing values generated)

{com}. recode cpcs_pre_std_upper50 (.=0)
{txt}(135 changes made to {bf:cpcs_pre_std_upper50})

{com}. rename cpcs_pre_std_upper50 CPCS_std_upper50
{res}{txt}
{com}. 
. 
. /// Cognitive
> wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 59
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 22
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2397567{col 38}{space 1}  -0.86{col 46}{space 3}0.412{col 54}{space 3}-.8430668{col 66}{space 3} .4660545
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_rses_u = e(N)
{txt}
{com}. scalar n_clust_cog_u_rses_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.3459862{col 38}{space 1}  -1.14{col 46}{space 3}0.270{col 54}{space 3}-.9499887{col 66}{space 3} .2893883
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_rses_l = e(N)
{txt}
{com}. scalar n_clust_cog_u_rses_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2419353{col 38}{space 1}  -0.87{col 46}{space 3}0.424{col 54}{space 3}-.8116933{col 66}{space 3} .4579261
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_rses_u = e(N)
{txt}
{com}. scalar n_clust_cog_l_rses_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0781293{col 38}{space 1}  -0.29{col 46}{space 3}0.790{col 54}{space 3}-.6381618{col 66}{space 3} .4659497
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_rses_l = e(N)
{txt}
{com}. scalar n_clust_cog_l_rses_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 55
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0533319{col 38}{space 1}   0.17{col 46}{space 3}0.888{col 54}{space 3}-.5684513{col 66}{space 3} .8328892
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_cpcs_u = e(N)
{txt}
{com}. scalar n_clust_cog_u_cpcs_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 67
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.5660393{col 38}{space 1}  -1.84{col 46}{space 3}0.086{col 54}{space 3}-1.164489{col 66}{space 3} .1582909
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_cpcs_l = e(N)
{txt}
{com}. scalar n_clust_cog_u_cpcs_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0295186{col 38}{space 1}   0.10{col 46}{space 3}0.930{col 54}{space 3}-.5959593{col 66}{space 3} .7286651
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_cpcs_u = e(N)
{txt}
{com}. scalar n_clust_cog_l_cpcs_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 68
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  -.19243{col 38}{space 1}  -0.73{col 46}{space 3}0.462{col 54}{space 3}-.7395154{col 66}{space 3} .3541719
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_cpcs_l = e(N)
{txt}
{com}. scalar n_clust_cog_l_cpcs_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. 
. 
. /// RSES
> wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .9579135{col 38}{space 1}   3.36{col 46}{space 3}0.006{col 54}{space 3} .2926921{col 66}{space 3} 1.618274
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_u_cog_u = e(N)
{txt}
{com}. scalar n_clust_rses_u_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 61
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0615952{col 38}{space 1}   0.32{col 46}{space 3}0.730{col 54}{space 3} -.357491{col 66}{space 3} .4662429
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_l_cog_u = e(N)
{txt}
{com}. scalar n_clust_rses_l_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .4920182{col 38}{space 1}   1.44{col 46}{space 3}0.218{col 54}{space 3}-.3341556{col 66}{space 3} 1.171978
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_u_cog_l = e(N)
{txt}
{com}. scalar n_clust_rses_u_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1388312{col 38}{space 1}   0.42{col 46}{space 3}0.724{col 54}{space 3}-.5788455{col 66}{space 3} .9515567
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_l_cog_l = e(N)
{txt}
{com}. scalar n_clust_rses_l_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 52
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 20
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .6664341{col 38}{space 1}   3.26{col 46}{space 3}0.002{col 54}{space 3} .2317593{col 66}{space 3} 1.089377
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3847293{col 38}{space 1}   1.53{col 46}{space 3}0.162{col 54}{space 3} -.129169{col 66}{space 3}  1.00823
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2062219{col 38}{space 1}   0.47{col 46}{space 3}0.676{col 54}{space 3}-.9608688{col 66}{space 3}  1.16695
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 66
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3332931{col 38}{space 1}   0.96{col 46}{space 3}0.406{col 54}{space 3}-.4731079{col 66}{space 3} 1.104825
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. 
. 
. /// CPCS
> 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} 1.020132{col 38}{space 1}   3.66{col 46}{space 3}0.002{col 54}{space 3} .3656306{col 66}{space 3} 1.674102
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 61
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0665639{col 38}{space 1}   0.34{col 46}{space 3}0.764{col 54}{space 3}-.3972062{col 66}{space 3} .4585619
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .5458399{col 38}{space 1}   1.63{col 46}{space 3}0.182{col 54}{space 3}-.3131337{col 66}{space 3} 1.237047
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1431476{col 38}{space 1}   0.41{col 46}{space 3}0.722{col 54}{space 3}-.6753389{col 66}{space 3}  1.01969
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 52
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 20
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  .774167{col 38}{space 1}   3.68{col 46}{space 3}0.004{col 54}{space 3} .3319338{col 66}{space 3} 1.286248
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_u_cog_u = e(N)
{txt}
{com}. scalar n_clust_cpcs_u_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3575321{col 38}{space 1}   1.51{col 46}{space 3}0.152{col 54}{space 3}-.1299744{col 66}{space 3} .8999454
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_l_cog_u = e(N)
{txt}
{com}. scalar n_clust_cpcs_l_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2792052{col 38}{space 1}   0.63{col 46}{space 3}0.554{col 54}{space 3}-.8493288{col 66}{space 3}   1.2984
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_u_cog_l = e(N)
{txt}
{com}. scalar n_clust_cpcs_u_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 66
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}   .33148{col 38}{space 1}   0.95{col 46}{space 3}0.402{col 54}{space 3}-.4205944{col 66}{space 3} 1.080687
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_l_cog_l = e(N)
{txt}
{com}. scalar n_clust_cpcs_l_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. // significant level
. 
. 
. local outcome cog rses cpcs
{txt}
{com}. local hetero1 cogU cogL
{txt}
{com}. local hetero2 rsesU rsesL
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. foreach h1 in `hetero1'{c -(}
{txt}  3{com}. foreach h2 in `hetero2'{c -(}
{txt}  4{com}.                 if `dep'_`h1'_`h2'_pv[1,1]<=0.01 {c -(}
{txt}  5{com}.                         local star_`dep'_`h1'_`h2' %3s "***"
{txt}  6{com}.                 {c )-}
{txt}  7{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.01) & (`dep'_`h1'_`h2'_pv[1,1]<=0.05) {c -(}
{txt}  8{com}.                         local star_`dep'_`h1'_`h2' %2s "**"
{txt}  9{com}.                 {c )-}
{txt} 10{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.05) & (`dep'_`h1'_`h2'_pv[1,1]<=0.10) {c -(}
{txt} 11{com}.                         local star_`dep'_`h1'_`h2' %1s "*"
{txt} 12{com}.                 {c )-}
{txt} 13{com}.                 else {c -(}
{txt} 14{com}.                         local star_`dep'_`h1'_`h2'  ""
{txt} 15{com}.                 {c )-}
{txt} 16{com}. {c )-} 
{txt} 17{com}. {c )-}
{txt} 18{com}. {c )-}
{txt}
{com}. 
. local outcome cog rses cpcs
{txt}
{com}. local hetero1 cogU cogL
{txt}
{com}. local hetero2 cpcsU cpcsL
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. foreach h1 in `hetero1'{c -(}
{txt}  3{com}. foreach h2 in `hetero2'{c -(}
{txt}  4{com}.                 if `dep'_`h1'_`h2'_pv[1,1]<=0.01 {c -(}
{txt}  5{com}.                         local star_`dep'_`h1'_`h2' %3s "***"
{txt}  6{com}.                 {c )-}
{txt}  7{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.01) & (`dep'_`h1'_`h2'_pv[1,1]<=0.05) {c -(}
{txt}  8{com}.                         local star_`dep'_`h1'_`h2' %2s "**"
{txt}  9{com}.                 {c )-}
{txt} 10{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.05) & (`dep'_`h1'_`h2'_pv[1,1]<=0.10) {c -(}
{txt} 11{com}.                         local star_`dep'_`h1'_`h2' %1s "*"
{txt} 12{com}.                 {c )-}
{txt} 13{com}.                 else {c -(}
{txt} 14{com}.                         local star_`dep'_`h1'_`h2'  ""
{txt} 15{com}.                 {c )-}
{txt} 16{com}. {c )-} 
{txt} 17{com}. {c )-}
{txt} 18{com}. {c )-}
{txt}
{com}. 
. /// Table
> 
. tempname hh2
{txt}
{com}. file open `hh2' using "$path_output/hetero_2by2_CPCS.tex", write replace
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "% Author: Kazuma Takakura" _newline
{txt}
{com}. file write `hh2' "% Date: `c(current_date)'" _newline
{txt}
{com}. file write `hh2' "% Time: `c(current_time)'" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. 
. file write `hh2' "\begin{c -(}table{c )-}[h!]\footnotesize" _newline
{txt}
{com}. file write `hh2' "  \centering" _newline
{txt}
{com}. file write `hh2' "  \caption{c -(}Heterogeneity among Baseline Abilites (Math and CPCS){c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:hetero_cpcs{c )-}" _newline
{txt}
{com}. file write `hh2' "\scalebox{c -(}1{c )-}{c -(}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}threeparttable{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "\begin{c -(}tabular{c )-}{c -(}lccccccc{c )-}\toprule" _newline
{txt}
{com}. 
. file write `hh2' " Dependent Variable & \multicolumn{c -(}2{c )-}{c -(}c{c )-}{c -(}Baseline^{c -(}b{c )-}{c )-} & Difference & Obs & N of clusters  \\\midrule\midrule" _newline
{txt}
{com}. file write `hh2' " Rapid math test score^{c -(}a{c )-} & Math Top 50\%  & CPCS Top 50\% & " %04.3f (cog_cogU_rsesU_mean[1,1]) `star_cog_cogU_cpcsU' " & " %02.0f ( n_cog_u_cpcs_u ) " & " %02.0f ( n_clust_cog_u_cpcs_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogU_cpcsU_pv[1,1]) " )  & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cog_cogU_cpcsL_mean[1,1]) `star_cog_cogU_cpcsL' " & " %02.0f ( n_cog_u_cpcs_l ) " & " %02.0f ( n_clust_cog_u_cpcs_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogU_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & Math Bottom 50\%  & CPCS Top 50\% & " %04.3f (cog_cogL_cpcsU_mean[1,1]) `star_cog_cogL_cpcsU' " & " %02.0f ( n_cog_l_cpcs_u ) " & " %02.0f ( n_clust_cog_l_cpcs_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogL_cpcsU_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cog_cogL_cpcsL_mean[1,1]) `star_cog_cogL_cpcsL' " & " %02.0f ( n_cog_l_cpcs_l ) " & " %02.0f ( n_clust_cog_l_cpcs_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogL_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. 
. file write `hh2' " CPCS score^{c -(}a{c )-} & Math Top 50\%  & CPCS Top 50\% & " %04.3f (cpcs_cogU_cpcsU_mean[1,1]) `star_cpcs_cogU_cpcsU' " & " %02.0f ( n_cpcs_u_cog_u ) " & " %02.0f ( n_clust_cpcs_u_cog_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cpcs_cogU_cpcsU_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cpcs_cogU_cpcsL_mean[1,1]) `star_cpcs_cogU_cpcsL' " & " %02.0f ( n_cpcs_l_cog_u ) " & " %02.0f ( n_clust_cpcs_l_cog_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &       & & ( " %04.3f (cpcs_cogU_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & Math Bottom 50\%  & CPCS Top 50\% & " %04.3f (cpcs_cogL_cpcsU_mean[1,1]) `star_cpcs_cogL_cpcsU' " & " %02.0f ( n_cpcs_u_cog_l ) " & " %02.0f ( n_clust_cpcs_u_cog_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cpcs_cogL_cpcsU_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cpcs_cogL_cpcsL_mean[1,1]) `star_cpcs_cogL_cpcsL' " & " %02.0f ( n_cpcs_l_cog_l ) " & " %02.0f ( n_clust_cpcs_l_cog_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &       & & ( " %04.3f (cpcs_cogL_cpcsL_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. 
. file write `hh2' "\\\midrule" _newline
{txt}
{com}. file write `hh2' "\end{c -(}tabular{c )-}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\item (a) Dependent variables are standardized using the average and variance of the whole follow-up sample in the February 2022 survey. " _newline
{txt}
{com}. file write `hh2' "\item (b) Cutoffs are created based on whether their ability to perform each item at the baseline was higher or lower than the median. " _newline
{txt}
{com}. file write `hh2' "\item (c) Wild cluster bootstrap p-values are reported within parentheses. Clusters are schools at the baseline." _newline
{txt}
{com}. file write `hh2' "\item (d) $^*$ Significant at 10\% level; $^{c -(}**{c )-}$ significant at 5\% level; $^{c -(}***{c )-}$ significant at 1\% level. " _newline
{txt}
{com}. file write `hh2' "\end{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}threeparttable{c )-}" _newline
{txt}
{com}. file write `hh2' "{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:addlabel{c )-}%" _newline
{txt}
{com}. file write `hh2' "\end{c -(}table{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. file close `hh2'
{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/mb/s9qljd594gbfyhl2dqnnwlcw0000gn/T//SD56186.000000"
{txt}
{com}. * This is the do file to create "Table 5. Heterogeneity by Baseline Abilites (Math and CPCS)"
. set seed 12345
{txt}
{com}. 
. use "$path_data/temp/followup_student_baseline_score_missing_dummy", replace
{txt}
{com}. 
. egen DT_score_pre_std_med = median(DT_score_pre_std)
{txt}
{com}. gen DT_score_pre_std_upper50 = 1 if DT_score_pre_std>DT_score_pre_std_med
{txt}(121 missing values generated)

{com}. recode DT_score_pre_std_upper50 (.=0)
{txt}(121 changes made to {bf:DT_score_pre_std_upper50})

{com}. 
. egen rosen_pre_std_med = median(rosen_pre_std)
{txt}
{com}. gen rosen_pre_std_upper50 = 1 if rosen_pre_std>rosen_pre_std_med
{txt}(128 missing values generated)

{com}. recode rosen_pre_std_upper50 (.=0)
{txt}(128 changes made to {bf:rosen_pre_std_upper50})

{com}. rename rosen_pre_std_upper50 RSES_std_upper50
{res}{txt}
{com}. 
. egen cpcs_pre_std_med = median(cpcs_pre_std)
{txt}
{com}. gen cpcs_pre_std_upper50 = 1 if cpcs_pre_std>cpcs_pre_std_med
{txt}(135 missing values generated)

{com}. recode cpcs_pre_std_upper50 (.=0)
{txt}(135 changes made to {bf:cpcs_pre_std_upper50})

{com}. rename cpcs_pre_std_upper50 CPCS_std_upper50
{res}{txt}
{com}. 
. 
. /// Cognitive
> wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 59
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 22
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2397567{col 38}{space 1}  -0.86{col 46}{space 3}0.408{col 54}{space 3}-.8202917{col 66}{space 3} .4030122
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_rses_u = e(N)
{txt}
{com}. scalar n_clust_cog_u_rses_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.3459862{col 38}{space 1}  -1.14{col 46}{space 3}0.300{col 54}{space 3}-1.047341{col 66}{space 3} .3326785
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_rses_l = e(N)
{txt}
{com}. scalar n_clust_cog_u_rses_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2419353{col 38}{space 1}  -0.87{col 46}{space 3}0.388{col 54}{space 3}-.7804587{col 66}{space 3} .3886955
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_rses_u = e(N)
{txt}
{com}. scalar n_clust_cog_l_rses_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0781293{col 38}{space 1}  -0.29{col 46}{space 3}0.782{col 54}{space 3}-.6060343{col 66}{space 3} .5369166
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_rses_l = e(N)
{txt}
{com}. scalar n_clust_cog_l_rses_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 55
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0533319{col 38}{space 1}   0.17{col 46}{space 3}0.816{col 54}{space 3}-.6322874{col 66}{space 3} .7715168
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_cpcs_u = e(N)
{txt}
{com}. scalar n_clust_cog_u_cpcs_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 67
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.5660393{col 38}{space 1}  -1.84{col 46}{space 3}0.098{col 54}{space 3}-1.176216{col 66}{space 3} .1246408
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_cpcs_l = e(N)
{txt}
{com}. scalar n_clust_cog_u_cpcs_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0295186{col 38}{space 1}   0.10{col 46}{space 3}0.906{col 54}{space 3} -.573143{col 66}{space 3} .8485677
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_cpcs_u = e(N)
{txt}
{com}. scalar n_clust_cog_l_cpcs_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 68
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  -.19243{col 38}{space 1}  -0.73{col 46}{space 3}0.476{col 54}{space 3} -.737602{col 66}{space 3} .3717598
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_cpcs_l = e(N)
{txt}
{com}. scalar n_clust_cog_l_cpcs_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. 
. 
. /// RSES
> wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .9579135{col 38}{space 1}   3.36{col 46}{space 3}0.012{col 54}{space 3} .2318333{col 66}{space 3} 1.609538
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_u_cog_u = e(N)
{txt}
{com}. scalar n_clust_rses_u_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 61
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0615952{col 38}{space 1}   0.32{col 46}{space 3}0.754{col 54}{space 3}-.3577834{col 66}{space 3} .5088901
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_l_cog_u = e(N)
{txt}
{com}. scalar n_clust_rses_l_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .4920182{col 38}{space 1}   1.44{col 46}{space 3}0.202{col 54}{space 3}-.3397751{col 66}{space 3} 1.201497
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_u_cog_l = e(N)
{txt}
{com}. scalar n_clust_rses_u_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1388312{col 38}{space 1}   0.42{col 46}{space 3}0.720{col 54}{space 3}-.5790653{col 66}{space 3} .8731936
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_l_cog_l = e(N)
{txt}
{com}. scalar n_clust_rses_l_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:19})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 52
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 20
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .6664341{col 38}{space 1}   3.26{col 46}{space 3}0.002{col 54}{space 3} .2148283{col 66}{space 3} 1.099697
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3847293{col 38}{space 1}   1.53{col 46}{space 3}0.120{col 54}{space 3}-.1707439{col 66}{space 3} .9620465
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2062219{col 38}{space 1}   0.47{col 46}{space 3}0.748{col 54}{space 3}-.8703771{col 66}{space 3} 1.127045
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 66
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3332931{col 38}{space 1}   0.96{col 46}{space 3}0.400{col 54}{space 3}-.4646233{col 66}{space 3} 1.063852
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. 
. 
. /// CPCS
> 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} 1.020132{col 38}{space 1}   3.66{col 46}{space 3}0.006{col 54}{space 3} .3791471{col 66}{space 3} 1.623668
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 61
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0665639{col 38}{space 1}   0.34{col 46}{space 3}0.806{col 54}{space 3}-.3635373{col 66}{space 3} .4574827
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .5458399{col 38}{space 1}   1.63{col 46}{space 3}0.116{col 54}{space 3}-.2282276{col 66}{space 3}  1.23819
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1431476{col 38}{space 1}   0.41{col 46}{space 3}0.726{col 54}{space 3}-.6540842{col 66}{space 3} 1.053531
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 52
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 20
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  .774167{col 38}{space 1}   3.68{col 46}{space 3}0.002{col 54}{space 3} .3331786{col 66}{space 3} 1.273728
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_u_cog_u = e(N)
{txt}
{com}. scalar n_clust_cpcs_u_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3575321{col 38}{space 1}   1.51{col 46}{space 3}0.154{col 54}{space 3}-.1513783{col 66}{space 3} .8594714
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_l_cog_u = e(N)
{txt}
{com}. scalar n_clust_cpcs_l_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2792052{col 38}{space 1}   0.63{col 46}{space 3}0.540{col 54}{space 3}-.8439628{col 66}{space 3} 1.257816
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_u_cog_l = e(N)
{txt}
{com}. scalar n_clust_cpcs_u_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 66
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}   .33148{col 38}{space 1}   0.95{col 46}{space 3}0.378{col 54}{space 3}-.3910148{col 66}{space 3} 1.055368
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_l_cog_l = e(N)
{txt}
{com}. scalar n_clust_cpcs_l_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. // significant level
. 
. 
. local outcome cog rses cpcs
{txt}
{com}. local hetero1 cogU cogL
{txt}
{com}. local hetero2 rsesU rsesL
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. foreach h1 in `hetero1'{c -(}
{txt}  3{com}. foreach h2 in `hetero2'{c -(}
{txt}  4{com}.                 if `dep'_`h1'_`h2'_pv[1,1]<=0.01 {c -(}
{txt}  5{com}.                         local star_`dep'_`h1'_`h2' %3s "***"
{txt}  6{com}.                 {c )-}
{txt}  7{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.01) & (`dep'_`h1'_`h2'_pv[1,1]<=0.05) {c -(}
{txt}  8{com}.                         local star_`dep'_`h1'_`h2' %2s "**"
{txt}  9{com}.                 {c )-}
{txt} 10{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.05) & (`dep'_`h1'_`h2'_pv[1,1]<=0.10) {c -(}
{txt} 11{com}.                         local star_`dep'_`h1'_`h2' %1s "*"
{txt} 12{com}.                 {c )-}
{txt} 13{com}.                 else {c -(}
{txt} 14{com}.                         local star_`dep'_`h1'_`h2'  ""
{txt} 15{com}.                 {c )-}
{txt} 16{com}. {c )-} 
{txt} 17{com}. {c )-}
{txt} 18{com}. {c )-}
{txt}
{com}. 
. local outcome cog rses cpcs
{txt}
{com}. local hetero1 cogU cogL
{txt}
{com}. local hetero2 cpcsU cpcsL
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. foreach h1 in `hetero1'{c -(}
{txt}  3{com}. foreach h2 in `hetero2'{c -(}
{txt}  4{com}.                 if `dep'_`h1'_`h2'_pv[1,1]<=0.01 {c -(}
{txt}  5{com}.                         local star_`dep'_`h1'_`h2' %3s "***"
{txt}  6{com}.                 {c )-}
{txt}  7{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.01) & (`dep'_`h1'_`h2'_pv[1,1]<=0.05) {c -(}
{txt}  8{com}.                         local star_`dep'_`h1'_`h2' %2s "**"
{txt}  9{com}.                 {c )-}
{txt} 10{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.05) & (`dep'_`h1'_`h2'_pv[1,1]<=0.10) {c -(}
{txt} 11{com}.                         local star_`dep'_`h1'_`h2' %1s "*"
{txt} 12{com}.                 {c )-}
{txt} 13{com}.                 else {c -(}
{txt} 14{com}.                         local star_`dep'_`h1'_`h2'  ""
{txt} 15{com}.                 {c )-}
{txt} 16{com}. {c )-} 
{txt} 17{com}. {c )-}
{txt} 18{com}. {c )-}
{txt}
{com}. 
. /// Table
> 
. tempname hh2
{txt}
{com}. file open `hh2' using "$path_output/hetero_2by2_CPCS.tex", write replace
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "% Author: Kazuma Takakura" _newline
{txt}
{com}. file write `hh2' "% Date: `c(current_date)'" _newline
{txt}
{com}. file write `hh2' "% Time: `c(current_time)'" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. 
. file write `hh2' "\begin{c -(}table{c )-}[h!]\footnotesize" _newline
{txt}
{com}. file write `hh2' "  \centering" _newline
{txt}
{com}. file write `hh2' "  \caption{c -(}Heterogeneity among Baseline Abilites (Math and CPCS){c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:hetero_cpcs{c )-}" _newline
{txt}
{com}. file write `hh2' "\scalebox{c -(}1{c )-}{c -(}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}threeparttable{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "\begin{c -(}tabular{c )-}{c -(}lccccccc{c )-}\toprule" _newline
{txt}
{com}. 
. file write `hh2' " Dependent Variable & \multicolumn{c -(}2{c )-}{c -(}c{c )-}{c -(}Baseline^{c -(}b{c )-}{c )-} & Difference & Obs & N of clusters  \\\midrule\midrule" _newline
{txt}
{com}. file write `hh2' " Rapid math test score^{c -(}a{c )-} & Math Top 50\%  & CPCS Top 50\% & " %04.3f (cog_cogU_rsesU_mean[1,1]) `star_cog_cogU_cpcsU' " & " %02.0f ( n_cog_u_cpcs_u ) " & " %02.0f ( n_clust_cog_u_cpcs_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogU_cpcsU_pv[1,1]) " )  & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cog_cogU_cpcsL_mean[1,1]) `star_cog_cogU_cpcsL' " & " %02.0f ( n_cog_u_cpcs_l ) " & " %02.0f ( n_clust_cog_u_cpcs_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogU_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & Math Bottom 50\%  & CPCS Top 50\% & " %04.3f (cog_cogL_cpcsU_mean[1,1]) `star_cog_cogL_cpcsU' " & " %02.0f ( n_cog_l_cpcs_u ) " & " %02.0f ( n_clust_cog_l_cpcs_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogL_cpcsU_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cog_cogL_cpcsL_mean[1,1]) `star_cog_cogL_cpcsL' " & " %02.0f ( n_cog_l_cpcs_l ) " & " %02.0f ( n_clust_cog_l_cpcs_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogL_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. 
. file write `hh2' " CPCS score^{c -(}a{c )-} & Math Top 50\%  & CPCS Top 50\% & " %04.3f (cpcs_cogU_cpcsU_mean[1,1]) `star_cpcs_cogU_cpcsU' " & " %02.0f ( n_cpcs_u_cog_u ) " & " %02.0f ( n_clust_cpcs_u_cog_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cpcs_cogU_cpcsU_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cpcs_cogU_cpcsL_mean[1,1]) `star_cpcs_cogU_cpcsL' " & " %02.0f ( n_cpcs_l_cog_u ) " & " %02.0f ( n_clust_cpcs_l_cog_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &       & & ( " %04.3f (cpcs_cogU_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & Math Bottom 50\%  & CPCS Top 50\% & " %04.3f (cpcs_cogL_cpcsU_mean[1,1]) `star_cpcs_cogL_cpcsU' " & " %02.0f ( n_cpcs_u_cog_l ) " & " %02.0f ( n_clust_cpcs_u_cog_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cpcs_cogL_cpcsU_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cpcs_cogL_cpcsL_mean[1,1]) `star_cpcs_cogL_cpcsL' " & " %02.0f ( n_cpcs_l_cog_l ) " & " %02.0f ( n_clust_cpcs_l_cog_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &       & & ( " %04.3f (cpcs_cogL_cpcsL_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. 
. file write `hh2' "\\\midrule" _newline
{txt}
{com}. file write `hh2' "\end{c -(}tabular{c )-}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\item (a) Dependent variables are standardized using the average and variance of the whole follow-up sample in the February 2022 survey. " _newline
{txt}
{com}. file write `hh2' "\item (b) Cutoffs are created based on whether their ability to perform each item at the baseline was higher or lower than the median. " _newline
{txt}
{com}. file write `hh2' "\item (c) Wild cluster bootstrap p-values are reported within parentheses. Clusters are schools at the baseline." _newline
{txt}
{com}. file write `hh2' "\item (d) $^*$ Significant at 10\% level; $^{c -(}**{c )-}$ significant at 5\% level; $^{c -(}***{c )-}$ significant at 1\% level. " _newline
{txt}
{com}. file write `hh2' "\end{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}threeparttable{c )-}" _newline
{txt}
{com}. file write `hh2' "{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:addlabel{c )-}%" _newline
{txt}
{com}. file write `hh2' "\end{c -(}table{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. file close `hh2'
{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/mb/s9qljd594gbfyhl2dqnnwlcw0000gn/T//SD56186.000000"
{txt}
{com}. * This is the do file to create "Table 5. Heterogeneity by Baseline Abilites (Math and CPCS)"
. set seed 123456
{txt}
{com}. 
. use "$path_data/temp/followup_student_baseline_score_missing_dummy", replace
{txt}
{com}. 
. egen DT_score_pre_std_med = median(DT_score_pre_std)
{txt}
{com}. gen DT_score_pre_std_upper50 = 1 if DT_score_pre_std>DT_score_pre_std_med
{txt}(121 missing values generated)

{com}. recode DT_score_pre_std_upper50 (.=0)
{txt}(121 changes made to {bf:DT_score_pre_std_upper50})

{com}. 
. egen rosen_pre_std_med = median(rosen_pre_std)
{txt}
{com}. gen rosen_pre_std_upper50 = 1 if rosen_pre_std>rosen_pre_std_med
{txt}(128 missing values generated)

{com}. recode rosen_pre_std_upper50 (.=0)
{txt}(128 changes made to {bf:rosen_pre_std_upper50})

{com}. rename rosen_pre_std_upper50 RSES_std_upper50
{res}{txt}
{com}. 
. egen cpcs_pre_std_med = median(cpcs_pre_std)
{txt}
{com}. gen cpcs_pre_std_upper50 = 1 if cpcs_pre_std>cpcs_pre_std_med
{txt}(135 missing values generated)

{com}. recode cpcs_pre_std_upper50 (.=0)
{txt}(135 changes made to {bf:cpcs_pre_std_upper50})

{com}. rename cpcs_pre_std_upper50 CPCS_std_upper50
{res}{txt}
{com}. 
. 
. /// Cognitive
> wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 59
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 22
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2397567{col 38}{space 1}  -0.86{col 46}{space 3}0.430{col 54}{space 3}-.8032403{col 66}{space 3} .3716752
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_rses_u = e(N)
{txt}
{com}. scalar n_clust_cog_u_rses_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:19})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.3459862{col 38}{space 1}  -1.14{col 46}{space 3}0.244{col 54}{space 3}-1.015253{col 66}{space 3} .2830204
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_rses_l = e(N)
{txt}
{com}. scalar n_clust_cog_u_rses_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2419353{col 38}{space 1}  -0.87{col 46}{space 3}0.410{col 54}{space 3}-.8329826{col 66}{space 3}  .438376
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_rses_u = e(N)
{txt}
{com}. scalar n_clust_cog_l_rses_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0781293{col 38}{space 1}  -0.29{col 46}{space 3}0.770{col 54}{space 3}-.5848532{col 66}{space 3}  .496441
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_rses_l = e(N)
{txt}
{com}. scalar n_clust_cog_l_rses_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 55
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0533319{col 38}{space 1}   0.17{col 46}{space 3}0.808{col 54}{space 3}-.6044358{col 66}{space 3} .7809246
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_cpcs_u = e(N)
{txt}
{com}. scalar n_clust_cog_u_cpcs_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 67
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.5660393{col 38}{space 1}  -1.84{col 46}{space 3}0.092{col 54}{space 3}-1.190451{col 66}{space 3} .1242038
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_cpcs_l = e(N)
{txt}
{com}. scalar n_clust_cog_u_cpcs_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0295186{col 38}{space 1}   0.10{col 46}{space 3}0.962{col 54}{space 3}-.5820749{col 66}{space 3} .7418611
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_cpcs_u = e(N)
{txt}
{com}. scalar n_clust_cog_l_cpcs_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 68
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  -.19243{col 38}{space 1}  -0.73{col 46}{space 3}0.484{col 54}{space 3}-.7372303{col 66}{space 3} .3723262
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_cpcs_l = e(N)
{txt}
{com}. scalar n_clust_cog_l_cpcs_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. 
. 
. /// RSES
> wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .9579135{col 38}{space 1}   3.36{col 46}{space 3}0.012{col 54}{space 3} .3102337{col 66}{space 3}  1.66492
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_u_cog_u = e(N)
{txt}
{com}. scalar n_clust_rses_u_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 61
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0615952{col 38}{space 1}   0.32{col 46}{space 3}0.750{col 54}{space 3}-.3644986{col 66}{space 3} .4502712
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_l_cog_u = e(N)
{txt}
{com}. scalar n_clust_rses_l_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .4920182{col 38}{space 1}   1.44{col 46}{space 3}0.188{col 54}{space 3}-.3104861{col 66}{space 3} 1.221096
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_u_cog_l = e(N)
{txt}
{com}. scalar n_clust_rses_u_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1388312{col 38}{space 1}   0.42{col 46}{space 3}0.682{col 54}{space 3}-.6196358{col 66}{space 3} .9070666
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_l_cog_l = e(N)
{txt}
{com}. scalar n_clust_rses_l_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 52
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 20
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .6664341{col 38}{space 1}   3.26{col 46}{space 3}0.008{col 54}{space 3} .2503958{col 66}{space 3} 1.149229
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3847293{col 38}{space 1}   1.53{col 46}{space 3}0.116{col 54}{space 3}-.0983357{col 66}{space 3}  .928789
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2062219{col 38}{space 1}   0.47{col 46}{space 3}0.712{col 54}{space 3}-.9964304{col 66}{space 3} 1.115433
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 66
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3332931{col 38}{space 1}   0.96{col 46}{space 3}0.424{col 54}{space 3}-.5309029{col 66}{space 3} 1.116114
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. 
. 
. /// CPCS
> 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} 1.020132{col 38}{space 1}   3.66{col 46}{space 3}0.002{col 54}{space 3} .3957498{col 66}{space 3} 1.677183
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 61
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0665639{col 38}{space 1}   0.34{col 46}{space 3}0.702{col 54}{space 3}-.3660282{col 66}{space 3}  .472553
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .5458399{col 38}{space 1}   1.63{col 46}{space 3}0.170{col 54}{space 3}-.2853186{col 66}{space 3} 1.213484
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1431476{col 38}{space 1}   0.41{col 46}{space 3}0.744{col 54}{space 3}-.6217227{col 66}{space 3} .9566137
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 52
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 20
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  .774167{col 38}{space 1}   3.68{col 46}{space 3}0.002{col 54}{space 3} .2954373{col 66}{space 3} 1.292805
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_u_cog_u = e(N)
{txt}
{com}. scalar n_clust_cpcs_u_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3575321{col 38}{space 1}   1.51{col 46}{space 3}0.134{col 54}{space 3}-.0968285{col 66}{space 3}  .850778
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_l_cog_u = e(N)
{txt}
{com}. scalar n_clust_cpcs_l_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2792052{col 38}{space 1}   0.63{col 46}{space 3}0.560{col 54}{space 3} -.891793{col 66}{space 3} 1.321435
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_u_cog_l = e(N)
{txt}
{com}. scalar n_clust_cpcs_u_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 66
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}   .33148{col 38}{space 1}   0.95{col 46}{space 3}0.422{col 54}{space 3}-.5339144{col 66}{space 3} 1.054402
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_l_cog_l = e(N)
{txt}
{com}. scalar n_clust_cpcs_l_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. // significant level
. 
. 
. local outcome cog rses cpcs
{txt}
{com}. local hetero1 cogU cogL
{txt}
{com}. local hetero2 rsesU rsesL
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. foreach h1 in `hetero1'{c -(}
{txt}  3{com}. foreach h2 in `hetero2'{c -(}
{txt}  4{com}.                 if `dep'_`h1'_`h2'_pv[1,1]<=0.01 {c -(}
{txt}  5{com}.                         local star_`dep'_`h1'_`h2' %3s "***"
{txt}  6{com}.                 {c )-}
{txt}  7{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.01) & (`dep'_`h1'_`h2'_pv[1,1]<=0.05) {c -(}
{txt}  8{com}.                         local star_`dep'_`h1'_`h2' %2s "**"
{txt}  9{com}.                 {c )-}
{txt} 10{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.05) & (`dep'_`h1'_`h2'_pv[1,1]<=0.10) {c -(}
{txt} 11{com}.                         local star_`dep'_`h1'_`h2' %1s "*"
{txt} 12{com}.                 {c )-}
{txt} 13{com}.                 else {c -(}
{txt} 14{com}.                         local star_`dep'_`h1'_`h2'  ""
{txt} 15{com}.                 {c )-}
{txt} 16{com}. {c )-} 
{txt} 17{com}. {c )-}
{txt} 18{com}. {c )-}
{txt}
{com}. 
. local outcome cog rses cpcs
{txt}
{com}. local hetero1 cogU cogL
{txt}
{com}. local hetero2 cpcsU cpcsL
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. foreach h1 in `hetero1'{c -(}
{txt}  3{com}. foreach h2 in `hetero2'{c -(}
{txt}  4{com}.                 if `dep'_`h1'_`h2'_pv[1,1]<=0.01 {c -(}
{txt}  5{com}.                         local star_`dep'_`h1'_`h2' %3s "***"
{txt}  6{com}.                 {c )-}
{txt}  7{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.01) & (`dep'_`h1'_`h2'_pv[1,1]<=0.05) {c -(}
{txt}  8{com}.                         local star_`dep'_`h1'_`h2' %2s "**"
{txt}  9{com}.                 {c )-}
{txt} 10{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.05) & (`dep'_`h1'_`h2'_pv[1,1]<=0.10) {c -(}
{txt} 11{com}.                         local star_`dep'_`h1'_`h2' %1s "*"
{txt} 12{com}.                 {c )-}
{txt} 13{com}.                 else {c -(}
{txt} 14{com}.                         local star_`dep'_`h1'_`h2'  ""
{txt} 15{com}.                 {c )-}
{txt} 16{com}. {c )-} 
{txt} 17{com}. {c )-}
{txt} 18{com}. {c )-}
{txt}
{com}. 
. /// Table
> 
. tempname hh2
{txt}
{com}. file open `hh2' using "$path_output/hetero_2by2_CPCS.tex", write replace
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "% Author: Kazuma Takakura" _newline
{txt}
{com}. file write `hh2' "% Date: `c(current_date)'" _newline
{txt}
{com}. file write `hh2' "% Time: `c(current_time)'" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. 
. file write `hh2' "\begin{c -(}table{c )-}[h!]\footnotesize" _newline
{txt}
{com}. file write `hh2' "  \centering" _newline
{txt}
{com}. file write `hh2' "  \caption{c -(}Heterogeneity among Baseline Abilites (Math and CPCS){c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:hetero_cpcs{c )-}" _newline
{txt}
{com}. file write `hh2' "\scalebox{c -(}1{c )-}{c -(}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}threeparttable{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "\begin{c -(}tabular{c )-}{c -(}lccccccc{c )-}\toprule" _newline
{txt}
{com}. 
. file write `hh2' " Dependent Variable & \multicolumn{c -(}2{c )-}{c -(}c{c )-}{c -(}Baseline^{c -(}b{c )-}{c )-} & Difference & Obs & N of clusters  \\\midrule\midrule" _newline
{txt}
{com}. file write `hh2' " Rapid math test score^{c -(}a{c )-} & Math Top 50\%  & CPCS Top 50\% & " %04.3f (cog_cogU_rsesU_mean[1,1]) `star_cog_cogU_cpcsU' " & " %02.0f ( n_cog_u_cpcs_u ) " & " %02.0f ( n_clust_cog_u_cpcs_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogU_cpcsU_pv[1,1]) " )  & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cog_cogU_cpcsL_mean[1,1]) `star_cog_cogU_cpcsL' " & " %02.0f ( n_cog_u_cpcs_l ) " & " %02.0f ( n_clust_cog_u_cpcs_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogU_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & Math Bottom 50\%  & CPCS Top 50\% & " %04.3f (cog_cogL_cpcsU_mean[1,1]) `star_cog_cogL_cpcsU' " & " %02.0f ( n_cog_l_cpcs_u ) " & " %02.0f ( n_clust_cog_l_cpcs_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogL_cpcsU_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cog_cogL_cpcsL_mean[1,1]) `star_cog_cogL_cpcsL' " & " %02.0f ( n_cog_l_cpcs_l ) " & " %02.0f ( n_clust_cog_l_cpcs_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogL_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. 
. file write `hh2' " CPCS score^{c -(}a{c )-} & Math Top 50\%  & CPCS Top 50\% & " %04.3f (cpcs_cogU_cpcsU_mean[1,1]) `star_cpcs_cogU_cpcsU' " & " %02.0f ( n_cpcs_u_cog_u ) " & " %02.0f ( n_clust_cpcs_u_cog_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cpcs_cogU_cpcsU_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cpcs_cogU_cpcsL_mean[1,1]) `star_cpcs_cogU_cpcsL' " & " %02.0f ( n_cpcs_l_cog_u ) " & " %02.0f ( n_clust_cpcs_l_cog_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &       & & ( " %04.3f (cpcs_cogU_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & Math Bottom 50\%  & CPCS Top 50\% & " %04.3f (cpcs_cogL_cpcsU_mean[1,1]) `star_cpcs_cogL_cpcsU' " & " %02.0f ( n_cpcs_u_cog_l ) " & " %02.0f ( n_clust_cpcs_u_cog_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cpcs_cogL_cpcsU_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cpcs_cogL_cpcsL_mean[1,1]) `star_cpcs_cogL_cpcsL' " & " %02.0f ( n_cpcs_l_cog_l ) " & " %02.0f ( n_clust_cpcs_l_cog_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &       & & ( " %04.3f (cpcs_cogL_cpcsL_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. 
. file write `hh2' "\\\midrule" _newline
{txt}
{com}. file write `hh2' "\end{c -(}tabular{c )-}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\item (a) Dependent variables are standardized using the average and variance of the whole follow-up sample in the February 2022 survey. " _newline
{txt}
{com}. file write `hh2' "\item (b) Cutoffs are created based on whether their ability to perform each item at the baseline was higher or lower than the median. " _newline
{txt}
{com}. file write `hh2' "\item (c) Wild cluster bootstrap p-values are reported within parentheses. Clusters are schools at the baseline." _newline
{txt}
{com}. file write `hh2' "\item (d) $^*$ Significant at 10\% level; $^{c -(}**{c )-}$ significant at 5\% level; $^{c -(}***{c )-}$ significant at 1\% level. " _newline
{txt}
{com}. file write `hh2' "\end{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}threeparttable{c )-}" _newline
{txt}
{com}. file write `hh2' "{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:addlabel{c )-}%" _newline
{txt}
{com}. file write `hh2' "\end{c -(}table{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. file close `hh2'
{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/mb/s9qljd594gbfyhl2dqnnwlcw0000gn/T//SD56186.000000"
{txt}
{com}. * This is the do file to create "Table 5. Heterogeneity by Baseline Abilites (Math and CPCS)"
. set seed 123
{txt}
{com}. 
. use "$path_data/temp/followup_student_baseline_score_missing_dummy", replace
{txt}
{com}. 
. egen DT_score_pre_std_med = median(DT_score_pre_std)
{txt}
{com}. gen DT_score_pre_std_upper50 = 1 if DT_score_pre_std>DT_score_pre_std_med
{txt}(121 missing values generated)

{com}. recode DT_score_pre_std_upper50 (.=0)
{txt}(121 changes made to {bf:DT_score_pre_std_upper50})

{com}. 
. egen rosen_pre_std_med = median(rosen_pre_std)
{txt}
{com}. gen rosen_pre_std_upper50 = 1 if rosen_pre_std>rosen_pre_std_med
{txt}(128 missing values generated)

{com}. recode rosen_pre_std_upper50 (.=0)
{txt}(128 changes made to {bf:rosen_pre_std_upper50})

{com}. rename rosen_pre_std_upper50 RSES_std_upper50
{res}{txt}
{com}. 
. egen cpcs_pre_std_med = median(cpcs_pre_std)
{txt}
{com}. gen cpcs_pre_std_upper50 = 1 if cpcs_pre_std>cpcs_pre_std_med
{txt}(135 missing values generated)

{com}. recode cpcs_pre_std_upper50 (.=0)
{txt}(135 changes made to {bf:cpcs_pre_std_upper50})

{com}. rename cpcs_pre_std_upper50 CPCS_std_upper50
{res}{txt}
{com}. 
. 
. /// Cognitive
> wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 59
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 22
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2397567{col 38}{space 1}  -0.86{col 46}{space 3}0.444{col 54}{space 3} -.835023{col 66}{space 3} .4309843
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_rses_u = e(N)
{txt}
{com}. scalar n_clust_cog_u_rses_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.3459862{col 38}{space 1}  -1.14{col 46}{space 3}0.246{col 54}{space 3}-.9646946{col 66}{space 3} .2967857
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_rses_l = e(N)
{txt}
{com}. scalar n_clust_cog_u_rses_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2419353{col 38}{space 1}  -0.87{col 46}{space 3}0.390{col 54}{space 3}-.8433267{col 66}{space 3} .3920268
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_rses_u = e(N)
{txt}
{com}. scalar n_clust_cog_l_rses_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0781293{col 38}{space 1}  -0.29{col 46}{space 3}0.794{col 54}{space 3}-.6038382{col 66}{space 3} .5443816
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_rses_l = e(N)
{txt}
{com}. scalar n_clust_cog_l_rses_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 55
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0533319{col 38}{space 1}   0.17{col 46}{space 3}0.872{col 54}{space 3}-.6242397{col 66}{space 3} .7907538
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_cpcs_u = e(N)
{txt}
{com}. scalar n_clust_cog_u_cpcs_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 67
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.5660393{col 38}{space 1}  -1.84{col 46}{space 3}0.092{col 54}{space 3}-1.245036{col 66}{space 3} .1419984
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_cpcs_l = e(N)
{txt}
{com}. scalar n_clust_cog_u_cpcs_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0295186{col 38}{space 1}   0.10{col 46}{space 3}0.942{col 54}{space 3}-.6165229{col 66}{space 3} .6514795
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_cpcs_u = e(N)
{txt}
{com}. scalar n_clust_cog_l_cpcs_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 68
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  -.19243{col 38}{space 1}  -0.73{col 46}{space 3}0.480{col 54}{space 3}-.7462179{col 66}{space 3} .3675341
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_cpcs_l = e(N)
{txt}
{com}. scalar n_clust_cog_l_cpcs_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. 
. 
. /// RSES
> wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .9579135{col 38}{space 1}   3.36{col 46}{space 3}0.000{col 54}{space 3} .3158411{col 66}{space 3} 1.651529
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_u_cog_u = e(N)
{txt}
{com}. scalar n_clust_rses_u_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 61
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0615952{col 38}{space 1}   0.32{col 46}{space 3}0.748{col 54}{space 3} -.335182{col 66}{space 3} .4398791
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_l_cog_u = e(N)
{txt}
{com}. scalar n_clust_rses_l_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:29})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .4920182{col 38}{space 1}   1.44{col 46}{space 3}0.152{col 54}{space 3}-.1956882{col 66}{space 3} 1.132075
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_u_cog_l = e(N)
{txt}
{com}. scalar n_clust_rses_u_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1388312{col 38}{space 1}   0.42{col 46}{space 3}0.748{col 54}{space 3}-.6586927{col 66}{space 3} .8819884
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_l_cog_l = e(N)
{txt}
{com}. scalar n_clust_rses_l_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 52
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 20
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .6664341{col 38}{space 1}   3.26{col 46}{space 3}0.004{col 54}{space 3} .2256924{col 66}{space 3}  1.16988
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3847293{col 38}{space 1}   1.53{col 46}{space 3}0.150{col 54}{space 3}-.1439999{col 66}{space 3} .9723153
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2062219{col 38}{space 1}   0.47{col 46}{space 3}0.630{col 54}{space 3}-.9392839{col 66}{space 3} 1.108811
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 66
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3332931{col 38}{space 1}   0.96{col 46}{space 3}0.416{col 54}{space 3}-.4864367{col 66}{space 3} 1.101715
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. 
. 
. /// CPCS
> 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} 1.020132{col 38}{space 1}   3.66{col 46}{space 3}0.002{col 54}{space 3} .4199485{col 66}{space 3} 1.782028
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 61
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0665639{col 38}{space 1}   0.34{col 46}{space 3}0.706{col 54}{space 3}-.3338298{col 66}{space 3} .4994486
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .5458399{col 38}{space 1}   1.63{col 46}{space 3}0.112{col 54}{space 3}-.2035501{col 66}{space 3} 1.209728
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1431476{col 38}{space 1}   0.41{col 46}{space 3}0.700{col 54}{space 3}-.6565782{col 66}{space 3} 1.032317
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:19})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 52
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 20
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  .774167{col 38}{space 1}   3.68{col 46}{space 3}0.006{col 54}{space 3}  .320576{col 66}{space 3} 1.297191
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_u_cog_u = e(N)
{txt}
{com}. scalar n_clust_cpcs_u_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3575321{col 38}{space 1}   1.51{col 46}{space 3}0.154{col 54}{space 3} -.150128{col 66}{space 3} .9146968
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_l_cog_u = e(N)
{txt}
{com}. scalar n_clust_cpcs_l_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2792052{col 38}{space 1}   0.63{col 46}{space 3}0.546{col 54}{space 3}-.9224898{col 66}{space 3} 1.348458
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_u_cog_l = e(N)
{txt}
{com}. scalar n_clust_cpcs_u_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 66
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}   .33148{col 38}{space 1}   0.95{col 46}{space 3}0.420{col 54}{space 3}-.5052153{col 66}{space 3} 1.053721
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_l_cog_l = e(N)
{txt}
{com}. scalar n_clust_cpcs_l_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. // significant level
. 
. 
. local outcome cog rses cpcs
{txt}
{com}. local hetero1 cogU cogL
{txt}
{com}. local hetero2 rsesU rsesL
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. foreach h1 in `hetero1'{c -(}
{txt}  3{com}. foreach h2 in `hetero2'{c -(}
{txt}  4{com}.                 if `dep'_`h1'_`h2'_pv[1,1]<=0.01 {c -(}
{txt}  5{com}.                         local star_`dep'_`h1'_`h2' %3s "***"
{txt}  6{com}.                 {c )-}
{txt}  7{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.01) & (`dep'_`h1'_`h2'_pv[1,1]<=0.05) {c -(}
{txt}  8{com}.                         local star_`dep'_`h1'_`h2' %2s "**"
{txt}  9{com}.                 {c )-}
{txt} 10{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.05) & (`dep'_`h1'_`h2'_pv[1,1]<=0.10) {c -(}
{txt} 11{com}.                         local star_`dep'_`h1'_`h2' %1s "*"
{txt} 12{com}.                 {c )-}
{txt} 13{com}.                 else {c -(}
{txt} 14{com}.                         local star_`dep'_`h1'_`h2'  ""
{txt} 15{com}.                 {c )-}
{txt} 16{com}. {c )-} 
{txt} 17{com}. {c )-}
{txt} 18{com}. {c )-}
{txt}
{com}. 
. local outcome cog rses cpcs
{txt}
{com}. local hetero1 cogU cogL
{txt}
{com}. local hetero2 cpcsU cpcsL
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. foreach h1 in `hetero1'{c -(}
{txt}  3{com}. foreach h2 in `hetero2'{c -(}
{txt}  4{com}.                 if `dep'_`h1'_`h2'_pv[1,1]<=0.01 {c -(}
{txt}  5{com}.                         local star_`dep'_`h1'_`h2' %3s "***"
{txt}  6{com}.                 {c )-}
{txt}  7{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.01) & (`dep'_`h1'_`h2'_pv[1,1]<=0.05) {c -(}
{txt}  8{com}.                         local star_`dep'_`h1'_`h2' %2s "**"
{txt}  9{com}.                 {c )-}
{txt} 10{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.05) & (`dep'_`h1'_`h2'_pv[1,1]<=0.10) {c -(}
{txt} 11{com}.                         local star_`dep'_`h1'_`h2' %1s "*"
{txt} 12{com}.                 {c )-}
{txt} 13{com}.                 else {c -(}
{txt} 14{com}.                         local star_`dep'_`h1'_`h2'  ""
{txt} 15{com}.                 {c )-}
{txt} 16{com}. {c )-} 
{txt} 17{com}. {c )-}
{txt} 18{com}. {c )-}
{txt}
{com}. 
. /// Table
> 
. tempname hh2
{txt}
{com}. file open `hh2' using "$path_output/hetero_2by2_CPCS.tex", write replace
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "% Author: Kazuma Takakura" _newline
{txt}
{com}. file write `hh2' "% Date: `c(current_date)'" _newline
{txt}
{com}. file write `hh2' "% Time: `c(current_time)'" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. 
. file write `hh2' "\begin{c -(}table{c )-}[h!]\footnotesize" _newline
{txt}
{com}. file write `hh2' "  \centering" _newline
{txt}
{com}. file write `hh2' "  \caption{c -(}Heterogeneity among Baseline Abilites (Math and CPCS){c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:hetero_cpcs{c )-}" _newline
{txt}
{com}. file write `hh2' "\scalebox{c -(}1{c )-}{c -(}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}threeparttable{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "\begin{c -(}tabular{c )-}{c -(}lccccccc{c )-}\toprule" _newline
{txt}
{com}. 
. file write `hh2' " Dependent Variable & \multicolumn{c -(}2{c )-}{c -(}c{c )-}{c -(}Baseline^{c -(}b{c )-}{c )-} & Difference & Obs & N of clusters  \\\midrule\midrule" _newline
{txt}
{com}. file write `hh2' " Rapid math test score^{c -(}a{c )-} & Math Top 50\%  & CPCS Top 50\% & " %04.3f (cog_cogU_rsesU_mean[1,1]) `star_cog_cogU_cpcsU' " & " %02.0f ( n_cog_u_cpcs_u ) " & " %02.0f ( n_clust_cog_u_cpcs_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogU_cpcsU_pv[1,1]) " )  & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cog_cogU_cpcsL_mean[1,1]) `star_cog_cogU_cpcsL' " & " %02.0f ( n_cog_u_cpcs_l ) " & " %02.0f ( n_clust_cog_u_cpcs_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogU_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & Math Bottom 50\%  & CPCS Top 50\% & " %04.3f (cog_cogL_cpcsU_mean[1,1]) `star_cog_cogL_cpcsU' " & " %02.0f ( n_cog_l_cpcs_u ) " & " %02.0f ( n_clust_cog_l_cpcs_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogL_cpcsU_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cog_cogL_cpcsL_mean[1,1]) `star_cog_cogL_cpcsL' " & " %02.0f ( n_cog_l_cpcs_l ) " & " %02.0f ( n_clust_cog_l_cpcs_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogL_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. 
. file write `hh2' " CPCS score^{c -(}a{c )-} & Math Top 50\%  & CPCS Top 50\% & " %04.3f (cpcs_cogU_cpcsU_mean[1,1]) `star_cpcs_cogU_cpcsU' " & " %02.0f ( n_cpcs_u_cog_u ) " & " %02.0f ( n_clust_cpcs_u_cog_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cpcs_cogU_cpcsU_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cpcs_cogU_cpcsL_mean[1,1]) `star_cpcs_cogU_cpcsL' " & " %02.0f ( n_cpcs_l_cog_u ) " & " %02.0f ( n_clust_cpcs_l_cog_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &       & & ( " %04.3f (cpcs_cogU_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & Math Bottom 50\%  & CPCS Top 50\% & " %04.3f (cpcs_cogL_cpcsU_mean[1,1]) `star_cpcs_cogL_cpcsU' " & " %02.0f ( n_cpcs_u_cog_l ) " & " %02.0f ( n_clust_cpcs_u_cog_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cpcs_cogL_cpcsU_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cpcs_cogL_cpcsL_mean[1,1]) `star_cpcs_cogL_cpcsL' " & " %02.0f ( n_cpcs_l_cog_l ) " & " %02.0f ( n_clust_cpcs_l_cog_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &       & & ( " %04.3f (cpcs_cogL_cpcsL_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. 
. file write `hh2' "\\\midrule" _newline
{txt}
{com}. file write `hh2' "\end{c -(}tabular{c )-}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\item (a) Dependent variables are standardized using the average and variance of the whole follow-up sample in the February 2022 survey. " _newline
{txt}
{com}. file write `hh2' "\item (b) Cutoffs are created based on whether their ability to perform each item at the baseline was higher or lower than the median. " _newline
{txt}
{com}. file write `hh2' "\item (c) Wild cluster bootstrap p-values are reported within parentheses. Clusters are schools at the baseline." _newline
{txt}
{com}. file write `hh2' "\item (d) $^*$ Significant at 10\% level; $^{c -(}**{c )-}$ significant at 5\% level; $^{c -(}***{c )-}$ significant at 1\% level. " _newline
{txt}
{com}. file write `hh2' "\end{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}threeparttable{c )-}" _newline
{txt}
{com}. file write `hh2' "{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:addlabel{c )-}%" _newline
{txt}
{com}. file write `hh2' "\end{c -(}table{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. file close `hh2'
{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/mb/s9qljd594gbfyhl2dqnnwlcw0000gn/T//SD56186.000000"
{txt}
{com}. * This is the do file to create "Table 5. Heterogeneity by Baseline Abilites (Math and CPCS)"
. set seed 11
{txt}
{com}. 
. use "$path_data/temp/followup_student_baseline_score_missing_dummy", replace
{txt}
{com}. 
. egen DT_score_pre_std_med = median(DT_score_pre_std)
{txt}
{com}. gen DT_score_pre_std_upper50 = 1 if DT_score_pre_std>DT_score_pre_std_med
{txt}(121 missing values generated)

{com}. recode DT_score_pre_std_upper50 (.=0)
{txt}(121 changes made to {bf:DT_score_pre_std_upper50})

{com}. 
. egen rosen_pre_std_med = median(rosen_pre_std)
{txt}
{com}. gen rosen_pre_std_upper50 = 1 if rosen_pre_std>rosen_pre_std_med
{txt}(128 missing values generated)

{com}. recode rosen_pre_std_upper50 (.=0)
{txt}(128 changes made to {bf:rosen_pre_std_upper50})

{com}. rename rosen_pre_std_upper50 RSES_std_upper50
{res}{txt}
{com}. 
. egen cpcs_pre_std_med = median(cpcs_pre_std)
{txt}
{com}. gen cpcs_pre_std_upper50 = 1 if cpcs_pre_std>cpcs_pre_std_med
{txt}(135 missing values generated)

{com}. recode cpcs_pre_std_upper50 (.=0)
{txt}(135 changes made to {bf:cpcs_pre_std_upper50})

{com}. rename cpcs_pre_std_upper50 CPCS_std_upper50
{res}{txt}
{com}. 
. 
. /// Cognitive
> wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 59
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 22
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2397567{col 38}{space 1}  -0.86{col 46}{space 3}0.416{col 54}{space 3}-.8196082{col 66}{space 3} .4222392
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_rses_u = e(N)
{txt}
{com}. scalar n_clust_cog_u_rses_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.3459862{col 38}{space 1}  -1.14{col 46}{space 3}0.300{col 54}{space 3}-1.012411{col 66}{space 3} .3691579
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_rses_l = e(N)
{txt}
{com}. scalar n_clust_cog_u_rses_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2419353{col 38}{space 1}  -0.87{col 46}{space 3}0.434{col 54}{space 3}-.8278847{col 66}{space 3} .4462468
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_rses_u = e(N)
{txt}
{com}. scalar n_clust_cog_l_rses_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0781293{col 38}{space 1}  -0.29{col 46}{space 3}0.802{col 54}{space 3}-.6138698{col 66}{space 3} .5792195
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_rses_l = e(N)
{txt}
{com}. scalar n_clust_cog_l_rses_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 55
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0533319{col 38}{space 1}   0.17{col 46}{space 3}0.868{col 54}{space 3}-.6060053{col 66}{space 3} .8090878
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_cpcs_u = e(N)
{txt}
{com}. scalar n_clust_cog_u_cpcs_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 67
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.5660393{col 38}{space 1}  -1.84{col 46}{space 3}0.092{col 54}{space 3}-1.210972{col 66}{space 3} .1180408
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_cpcs_l = e(N)
{txt}
{com}. scalar n_clust_cog_u_cpcs_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0295186{col 38}{space 1}   0.10{col 46}{space 3}0.960{col 54}{space 3}-.5782252{col 66}{space 3} .8270286
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_cpcs_u = e(N)
{txt}
{com}. scalar n_clust_cog_l_cpcs_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 68
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  -.19243{col 38}{space 1}  -0.73{col 46}{space 3}0.532{col 54}{space 3}-.6983148{col 66}{space 3} .4037461
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_cpcs_l = e(N)
{txt}
{com}. scalar n_clust_cog_l_cpcs_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. 
. 
. /// RSES
> wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .9579135{col 38}{space 1}   3.36{col 46}{space 3}0.004{col 54}{space 3} .3294795{col 66}{space 3} 1.578448
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_u_cog_u = e(N)
{txt}
{com}. scalar n_clust_rses_u_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 61
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0615952{col 38}{space 1}   0.32{col 46}{space 3}0.804{col 54}{space 3}-.3639511{col 66}{space 3} .4619739
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_l_cog_u = e(N)
{txt}
{com}. scalar n_clust_rses_l_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .4920182{col 38}{space 1}   1.44{col 46}{space 3}0.192{col 54}{space 3}-.2793967{col 66}{space 3} 1.174508
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_u_cog_l = e(N)
{txt}
{com}. scalar n_clust_rses_u_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1388312{col 38}{space 1}   0.42{col 46}{space 3}0.718{col 54}{space 3} -.677151{col 66}{space 3} .9173893
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_l_cog_l = e(N)
{txt}
{com}. scalar n_clust_rses_l_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:18})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 52
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 20
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .6664341{col 38}{space 1}   3.26{col 46}{space 3}0.006{col 54}{space 3} .2379325{col 66}{space 3} 1.114655
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3847293{col 38}{space 1}   1.53{col 46}{space 3}0.130{col 54}{space 3} -.138418{col 66}{space 3} .9704357
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2062219{col 38}{space 1}   0.47{col 46}{space 3}0.646{col 54}{space 3}-.9102324{col 66}{space 3} 1.139812
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 66
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3332931{col 38}{space 1}   0.96{col 46}{space 3}0.402{col 54}{space 3}-.4197598{col 66}{space 3} 1.068587
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. 
. 
. /// CPCS
> 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} 1.020132{col 38}{space 1}   3.66{col 46}{space 3}0.004{col 54}{space 3} .4155501{col 66}{space 3} 1.704039
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 61
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0665639{col 38}{space 1}   0.34{col 46}{space 3}0.694{col 54}{space 3}-.3321192{col 66}{space 3} .4823172
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:19})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .5458399{col 38}{space 1}   1.63{col 46}{space 3}0.130{col 54}{space 3}-.2322876{col 66}{space 3} 1.229615
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1431476{col 38}{space 1}   0.41{col 46}{space 3}0.734{col 54}{space 3}-.6953281{col 66}{space 3} .9991188
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 52
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 20
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  .774167{col 38}{space 1}   3.68{col 46}{space 3}0.002{col 54}{space 3} .3601224{col 66}{space 3} 1.231264
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_u_cog_u = e(N)
{txt}
{com}. scalar n_clust_cpcs_u_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}30{text}.{text} done{text} ({result:31})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3575321{col 38}{space 1}   1.51{col 46}{space 3}0.164{col 54}{space 3}-.1047515{col 66}{space 3} .8461828
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_l_cog_u = e(N)
{txt}
{com}. scalar n_clust_cpcs_l_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2792052{col 38}{space 1}   0.63{col 46}{space 3}0.576{col 54}{space 3}-.7899816{col 66}{space 3} 1.222768
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_u_cog_l = e(N)
{txt}
{com}. scalar n_clust_cpcs_u_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 66
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}   .33148{col 38}{space 1}   0.95{col 46}{space 3}0.396{col 54}{space 3}-.4281927{col 66}{space 3} 1.101473
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_l_cog_l = e(N)
{txt}
{com}. scalar n_clust_cpcs_l_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. // significant level
. 
. 
. local outcome cog rses cpcs
{txt}
{com}. local hetero1 cogU cogL
{txt}
{com}. local hetero2 rsesU rsesL
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. foreach h1 in `hetero1'{c -(}
{txt}  3{com}. foreach h2 in `hetero2'{c -(}
{txt}  4{com}.                 if `dep'_`h1'_`h2'_pv[1,1]<=0.01 {c -(}
{txt}  5{com}.                         local star_`dep'_`h1'_`h2' %3s "***"
{txt}  6{com}.                 {c )-}
{txt}  7{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.01) & (`dep'_`h1'_`h2'_pv[1,1]<=0.05) {c -(}
{txt}  8{com}.                         local star_`dep'_`h1'_`h2' %2s "**"
{txt}  9{com}.                 {c )-}
{txt} 10{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.05) & (`dep'_`h1'_`h2'_pv[1,1]<=0.10) {c -(}
{txt} 11{com}.                         local star_`dep'_`h1'_`h2' %1s "*"
{txt} 12{com}.                 {c )-}
{txt} 13{com}.                 else {c -(}
{txt} 14{com}.                         local star_`dep'_`h1'_`h2'  ""
{txt} 15{com}.                 {c )-}
{txt} 16{com}. {c )-} 
{txt} 17{com}. {c )-}
{txt} 18{com}. {c )-}
{txt}
{com}. 
. local outcome cog rses cpcs
{txt}
{com}. local hetero1 cogU cogL
{txt}
{com}. local hetero2 cpcsU cpcsL
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. foreach h1 in `hetero1'{c -(}
{txt}  3{com}. foreach h2 in `hetero2'{c -(}
{txt}  4{com}.                 if `dep'_`h1'_`h2'_pv[1,1]<=0.01 {c -(}
{txt}  5{com}.                         local star_`dep'_`h1'_`h2' %3s "***"
{txt}  6{com}.                 {c )-}
{txt}  7{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.01) & (`dep'_`h1'_`h2'_pv[1,1]<=0.05) {c -(}
{txt}  8{com}.                         local star_`dep'_`h1'_`h2' %2s "**"
{txt}  9{com}.                 {c )-}
{txt} 10{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.05) & (`dep'_`h1'_`h2'_pv[1,1]<=0.10) {c -(}
{txt} 11{com}.                         local star_`dep'_`h1'_`h2' %1s "*"
{txt} 12{com}.                 {c )-}
{txt} 13{com}.                 else {c -(}
{txt} 14{com}.                         local star_`dep'_`h1'_`h2'  ""
{txt} 15{com}.                 {c )-}
{txt} 16{com}. {c )-} 
{txt} 17{com}. {c )-}
{txt} 18{com}. {c )-}
{txt}
{com}. 
. /// Table
> 
. tempname hh2
{txt}
{com}. file open `hh2' using "$path_output/hetero_2by2_CPCS.tex", write replace
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "% Author: Kazuma Takakura" _newline
{txt}
{com}. file write `hh2' "% Date: `c(current_date)'" _newline
{txt}
{com}. file write `hh2' "% Time: `c(current_time)'" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. 
. file write `hh2' "\begin{c -(}table{c )-}[h!]\footnotesize" _newline
{txt}
{com}. file write `hh2' "  \centering" _newline
{txt}
{com}. file write `hh2' "  \caption{c -(}Heterogeneity among Baseline Abilites (Math and CPCS){c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:hetero_cpcs{c )-}" _newline
{txt}
{com}. file write `hh2' "\scalebox{c -(}1{c )-}{c -(}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}threeparttable{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "\begin{c -(}tabular{c )-}{c -(}lccccccc{c )-}\toprule" _newline
{txt}
{com}. 
. file write `hh2' " Dependent Variable & \multicolumn{c -(}2{c )-}{c -(}c{c )-}{c -(}Baseline^{c -(}b{c )-}{c )-} & Difference & Obs & N of clusters  \\\midrule\midrule" _newline
{txt}
{com}. file write `hh2' " Rapid math test score^{c -(}a{c )-} & Math Top 50\%  & CPCS Top 50\% & " %04.3f (cog_cogU_rsesU_mean[1,1]) `star_cog_cogU_cpcsU' " & " %02.0f ( n_cog_u_cpcs_u ) " & " %02.0f ( n_clust_cog_u_cpcs_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogU_cpcsU_pv[1,1]) " )  & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cog_cogU_cpcsL_mean[1,1]) `star_cog_cogU_cpcsL' " & " %02.0f ( n_cog_u_cpcs_l ) " & " %02.0f ( n_clust_cog_u_cpcs_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogU_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & Math Bottom 50\%  & CPCS Top 50\% & " %04.3f (cog_cogL_cpcsU_mean[1,1]) `star_cog_cogL_cpcsU' " & " %02.0f ( n_cog_l_cpcs_u ) " & " %02.0f ( n_clust_cog_l_cpcs_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogL_cpcsU_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cog_cogL_cpcsL_mean[1,1]) `star_cog_cogL_cpcsL' " & " %02.0f ( n_cog_l_cpcs_l ) " & " %02.0f ( n_clust_cog_l_cpcs_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogL_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. 
. file write `hh2' " CPCS score^{c -(}a{c )-} & Math Top 50\%  & CPCS Top 50\% & " %04.3f (cpcs_cogU_cpcsU_mean[1,1]) `star_cpcs_cogU_cpcsU' " & " %02.0f ( n_cpcs_u_cog_u ) " & " %02.0f ( n_clust_cpcs_u_cog_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cpcs_cogU_cpcsU_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cpcs_cogU_cpcsL_mean[1,1]) `star_cpcs_cogU_cpcsL' " & " %02.0f ( n_cpcs_l_cog_u ) " & " %02.0f ( n_clust_cpcs_l_cog_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &       & & ( " %04.3f (cpcs_cogU_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & Math Bottom 50\%  & CPCS Top 50\% & " %04.3f (cpcs_cogL_cpcsU_mean[1,1]) `star_cpcs_cogL_cpcsU' " & " %02.0f ( n_cpcs_u_cog_l ) " & " %02.0f ( n_clust_cpcs_u_cog_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cpcs_cogL_cpcsU_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cpcs_cogL_cpcsL_mean[1,1]) `star_cpcs_cogL_cpcsL' " & " %02.0f ( n_cpcs_l_cog_l ) " & " %02.0f ( n_clust_cpcs_l_cog_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &       & & ( " %04.3f (cpcs_cogL_cpcsL_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. 
. file write `hh2' "\\\midrule" _newline
{txt}
{com}. file write `hh2' "\end{c -(}tabular{c )-}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\item (a) Dependent variables are standardized using the average and variance of the whole follow-up sample in the February 2022 survey. " _newline
{txt}
{com}. file write `hh2' "\item (b) Cutoffs are created based on whether their ability to perform each item at the baseline was higher or lower than the median. " _newline
{txt}
{com}. file write `hh2' "\item (c) Wild cluster bootstrap p-values are reported within parentheses. Clusters are schools at the baseline." _newline
{txt}
{com}. file write `hh2' "\item (d) $^*$ Significant at 10\% level; $^{c -(}**{c )-}$ significant at 5\% level; $^{c -(}***{c )-}$ significant at 1\% level. " _newline
{txt}
{com}. file write `hh2' "\end{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}threeparttable{c )-}" _newline
{txt}
{com}. file write `hh2' "{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:addlabel{c )-}%" _newline
{txt}
{com}. file write `hh2' "\end{c -(}table{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. file close `hh2'
{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/mb/s9qljd594gbfyhl2dqnnwlcw0000gn/T//SD56186.000000"
{txt}
{com}. * This is the do file to create "Table 5. Heterogeneity by Baseline Abilites (Math and CPCS)"
. set seed 111
{txt}
{com}. 
. use "$path_data/temp/followup_student_baseline_score_missing_dummy", replace
{txt}
{com}. 
. egen DT_score_pre_std_med = median(DT_score_pre_std)
{txt}
{com}. gen DT_score_pre_std_upper50 = 1 if DT_score_pre_std>DT_score_pre_std_med
{txt}(121 missing values generated)

{com}. recode DT_score_pre_std_upper50 (.=0)
{txt}(121 changes made to {bf:DT_score_pre_std_upper50})

{com}. 
. egen rosen_pre_std_med = median(rosen_pre_std)
{txt}
{com}. gen rosen_pre_std_upper50 = 1 if rosen_pre_std>rosen_pre_std_med
{txt}(128 missing values generated)

{com}. recode rosen_pre_std_upper50 (.=0)
{txt}(128 changes made to {bf:rosen_pre_std_upper50})

{com}. rename rosen_pre_std_upper50 RSES_std_upper50
{res}{txt}
{com}. 
. egen cpcs_pre_std_med = median(cpcs_pre_std)
{txt}
{com}. gen cpcs_pre_std_upper50 = 1 if cpcs_pre_std>cpcs_pre_std_med
{txt}(135 missing values generated)

{com}. recode cpcs_pre_std_upper50 (.=0)
{txt}(135 changes made to {bf:cpcs_pre_std_upper50})

{com}. rename cpcs_pre_std_upper50 CPCS_std_upper50
{res}{txt}
{com}. 
. 
. /// Cognitive
> wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 59
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 22
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2397567{col 38}{space 1}  -0.86{col 46}{space 3}0.418{col 54}{space 3}-.7966316{col 66}{space 3}  .434311
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_rses_u = e(N)
{txt}
{com}. scalar n_clust_cog_u_rses_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.3459862{col 38}{space 1}  -1.14{col 46}{space 3}0.274{col 54}{space 3}-1.046262{col 66}{space 3}   .30228
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_rses_l = e(N)
{txt}
{com}. scalar n_clust_cog_u_rses_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2419353{col 38}{space 1}  -0.87{col 46}{space 3}0.364{col 54}{space 3}-.8619956{col 66}{space 3} .3835373
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_rses_u = e(N)
{txt}
{com}. scalar n_clust_cog_l_rses_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0781293{col 38}{space 1}  -0.29{col 46}{space 3}0.764{col 54}{space 3}-.6361322{col 66}{space 3} .4379878
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_rses_l = e(N)
{txt}
{com}. scalar n_clust_cog_l_rses_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:29})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 55
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0533319{col 38}{space 1}   0.17{col 46}{space 3}0.826{col 54}{space 3}-.6512296{col 66}{space 3} .8167328
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_cpcs_u = e(N)
{txt}
{com}. scalar n_clust_cog_u_cpcs_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 67
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.5660393{col 38}{space 1}  -1.84{col 46}{space 3}0.098{col 54}{space 3}-1.240136{col 66}{space 3}  .119599
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_cpcs_l = e(N)
{txt}
{com}. scalar n_clust_cog_u_cpcs_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0295186{col 38}{space 1}   0.10{col 46}{space 3}0.898{col 54}{space 3}-.5958441{col 66}{space 3} .6626134
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_cpcs_u = e(N)
{txt}
{com}. scalar n_clust_cog_l_cpcs_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:19})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 68
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  -.19243{col 38}{space 1}  -0.73{col 46}{space 3}0.482{col 54}{space 3}-.7246009{col 66}{space 3} .3950123
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_cpcs_l = e(N)
{txt}
{com}. scalar n_clust_cog_l_cpcs_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. 
. 
. /// RSES
> wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .9579135{col 38}{space 1}   3.36{col 46}{space 3}0.016{col 54}{space 3}   .26476{col 66}{space 3} 1.654666
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_u_cog_u = e(N)
{txt}
{com}. scalar n_clust_rses_u_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 61
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0615952{col 38}{space 1}   0.32{col 46}{space 3}0.746{col 54}{space 3}-.3207408{col 66}{space 3} .4426532
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_l_cog_u = e(N)
{txt}
{com}. scalar n_clust_rses_l_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .4920182{col 38}{space 1}   1.44{col 46}{space 3}0.206{col 54}{space 3}-.3791899{col 66}{space 3} 1.193207
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_u_cog_l = e(N)
{txt}
{com}. scalar n_clust_rses_u_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1388312{col 38}{space 1}   0.42{col 46}{space 3}0.678{col 54}{space 3}-.6031055{col 66}{space 3}  .913588
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_l_cog_l = e(N)
{txt}
{com}. scalar n_clust_rses_l_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:19})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 52
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 20
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .6664341{col 38}{space 1}   3.26{col 46}{space 3}0.012{col 54}{space 3} .2321136{col 66}{space 3} 1.106288
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3847293{col 38}{space 1}   1.53{col 46}{space 3}0.124{col 54}{space 3}-.1218257{col 66}{space 3} .9801306
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2062219{col 38}{space 1}   0.47{col 46}{space 3}0.686{col 54}{space 3}-1.027415{col 66}{space 3} 1.180279
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 66
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3332931{col 38}{space 1}   0.96{col 46}{space 3}0.380{col 54}{space 3}-.4877823{col 66}{space 3} 1.105785
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. 
. 
. /// CPCS
> 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} 1.020132{col 38}{space 1}   3.66{col 46}{space 3}0.006{col 54}{space 3} .3600309{col 66}{space 3} 1.627818
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 61
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0665639{col 38}{space 1}   0.34{col 46}{space 3}0.810{col 54}{space 3}-.3876734{col 66}{space 3} .4535088
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .5458399{col 38}{space 1}   1.63{col 46}{space 3}0.144{col 54}{space 3}-.2669703{col 66}{space 3} 1.253775
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1431476{col 38}{space 1}   0.41{col 46}{space 3}0.704{col 54}{space 3}-.6225786{col 66}{space 3} 1.047643
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 52
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 20
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  .774167{col 38}{space 1}   3.68{col 46}{space 3}0.002{col 54}{space 3} .3283526{col 66}{space 3} 1.235857
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_u_cog_u = e(N)
{txt}
{com}. scalar n_clust_cpcs_u_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3575321{col 38}{space 1}   1.51{col 46}{space 3}0.162{col 54}{space 3}-.1390679{col 66}{space 3} .8784332
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_l_cog_u = e(N)
{txt}
{com}. scalar n_clust_cpcs_l_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2792052{col 38}{space 1}   0.63{col 46}{space 3}0.554{col 54}{space 3}-.8321789{col 66}{space 3} 1.362015
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_u_cog_l = e(N)
{txt}
{com}. scalar n_clust_cpcs_u_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 66
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}   .33148{col 38}{space 1}   0.95{col 46}{space 3}0.352{col 54}{space 3}-.4706789{col 66}{space 3} 1.158291
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_l_cog_l = e(N)
{txt}
{com}. scalar n_clust_cpcs_l_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. // significant level
. 
. 
. local outcome cog rses cpcs
{txt}
{com}. local hetero1 cogU cogL
{txt}
{com}. local hetero2 rsesU rsesL
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. foreach h1 in `hetero1'{c -(}
{txt}  3{com}. foreach h2 in `hetero2'{c -(}
{txt}  4{com}.                 if `dep'_`h1'_`h2'_pv[1,1]<=0.01 {c -(}
{txt}  5{com}.                         local star_`dep'_`h1'_`h2' %3s "***"
{txt}  6{com}.                 {c )-}
{txt}  7{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.01) & (`dep'_`h1'_`h2'_pv[1,1]<=0.05) {c -(}
{txt}  8{com}.                         local star_`dep'_`h1'_`h2' %2s "**"
{txt}  9{com}.                 {c )-}
{txt} 10{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.05) & (`dep'_`h1'_`h2'_pv[1,1]<=0.10) {c -(}
{txt} 11{com}.                         local star_`dep'_`h1'_`h2' %1s "*"
{txt} 12{com}.                 {c )-}
{txt} 13{com}.                 else {c -(}
{txt} 14{com}.                         local star_`dep'_`h1'_`h2'  ""
{txt} 15{com}.                 {c )-}
{txt} 16{com}. {c )-} 
{txt} 17{com}. {c )-}
{txt} 18{com}. {c )-}
{txt}
{com}. 
. local outcome cog rses cpcs
{txt}
{com}. local hetero1 cogU cogL
{txt}
{com}. local hetero2 cpcsU cpcsL
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. foreach h1 in `hetero1'{c -(}
{txt}  3{com}. foreach h2 in `hetero2'{c -(}
{txt}  4{com}.                 if `dep'_`h1'_`h2'_pv[1,1]<=0.01 {c -(}
{txt}  5{com}.                         local star_`dep'_`h1'_`h2' %3s "***"
{txt}  6{com}.                 {c )-}
{txt}  7{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.01) & (`dep'_`h1'_`h2'_pv[1,1]<=0.05) {c -(}
{txt}  8{com}.                         local star_`dep'_`h1'_`h2' %2s "**"
{txt}  9{com}.                 {c )-}
{txt} 10{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.05) & (`dep'_`h1'_`h2'_pv[1,1]<=0.10) {c -(}
{txt} 11{com}.                         local star_`dep'_`h1'_`h2' %1s "*"
{txt} 12{com}.                 {c )-}
{txt} 13{com}.                 else {c -(}
{txt} 14{com}.                         local star_`dep'_`h1'_`h2'  ""
{txt} 15{com}.                 {c )-}
{txt} 16{com}. {c )-} 
{txt} 17{com}. {c )-}
{txt} 18{com}. {c )-}
{txt}
{com}. 
. /// Table
> 
. tempname hh2
{txt}
{com}. file open `hh2' using "$path_output/hetero_2by2_CPCS.tex", write replace
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "% Author: Kazuma Takakura" _newline
{txt}
{com}. file write `hh2' "% Date: `c(current_date)'" _newline
{txt}
{com}. file write `hh2' "% Time: `c(current_time)'" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. 
. file write `hh2' "\begin{c -(}table{c )-}[h!]\footnotesize" _newline
{txt}
{com}. file write `hh2' "  \centering" _newline
{txt}
{com}. file write `hh2' "  \caption{c -(}Heterogeneity among Baseline Abilites (Math and CPCS){c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:hetero_cpcs{c )-}" _newline
{txt}
{com}. file write `hh2' "\scalebox{c -(}1{c )-}{c -(}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}threeparttable{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "\begin{c -(}tabular{c )-}{c -(}lccccccc{c )-}\toprule" _newline
{txt}
{com}. 
. file write `hh2' " Dependent Variable & \multicolumn{c -(}2{c )-}{c -(}c{c )-}{c -(}Baseline^{c -(}b{c )-}{c )-} & Difference & Obs & N of clusters  \\\midrule\midrule" _newline
{txt}
{com}. file write `hh2' " Rapid math test score^{c -(}a{c )-} & Math Top 50\%  & CPCS Top 50\% & " %04.3f (cog_cogU_rsesU_mean[1,1]) `star_cog_cogU_cpcsU' " & " %02.0f ( n_cog_u_cpcs_u ) " & " %02.0f ( n_clust_cog_u_cpcs_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogU_cpcsU_pv[1,1]) " )  & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cog_cogU_cpcsL_mean[1,1]) `star_cog_cogU_cpcsL' " & " %02.0f ( n_cog_u_cpcs_l ) " & " %02.0f ( n_clust_cog_u_cpcs_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogU_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & Math Bottom 50\%  & CPCS Top 50\% & " %04.3f (cog_cogL_cpcsU_mean[1,1]) `star_cog_cogL_cpcsU' " & " %02.0f ( n_cog_l_cpcs_u ) " & " %02.0f ( n_clust_cog_l_cpcs_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogL_cpcsU_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cog_cogL_cpcsL_mean[1,1]) `star_cog_cogL_cpcsL' " & " %02.0f ( n_cog_l_cpcs_l ) " & " %02.0f ( n_clust_cog_l_cpcs_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogL_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. 
. file write `hh2' " CPCS score^{c -(}a{c )-} & Math Top 50\%  & CPCS Top 50\% & " %04.3f (cpcs_cogU_cpcsU_mean[1,1]) `star_cpcs_cogU_cpcsU' " & " %02.0f ( n_cpcs_u_cog_u ) " & " %02.0f ( n_clust_cpcs_u_cog_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cpcs_cogU_cpcsU_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cpcs_cogU_cpcsL_mean[1,1]) `star_cpcs_cogU_cpcsL' " & " %02.0f ( n_cpcs_l_cog_u ) " & " %02.0f ( n_clust_cpcs_l_cog_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &       & & ( " %04.3f (cpcs_cogU_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & Math Bottom 50\%  & CPCS Top 50\% & " %04.3f (cpcs_cogL_cpcsU_mean[1,1]) `star_cpcs_cogL_cpcsU' " & " %02.0f ( n_cpcs_u_cog_l ) " & " %02.0f ( n_clust_cpcs_u_cog_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cpcs_cogL_cpcsU_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cpcs_cogL_cpcsL_mean[1,1]) `star_cpcs_cogL_cpcsL' " & " %02.0f ( n_cpcs_l_cog_l ) " & " %02.0f ( n_clust_cpcs_l_cog_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &       & & ( " %04.3f (cpcs_cogL_cpcsL_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. 
. file write `hh2' "\\\midrule" _newline
{txt}
{com}. file write `hh2' "\end{c -(}tabular{c )-}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\item (a) Dependent variables are standardized using the average and variance of the whole follow-up sample in the February 2022 survey. " _newline
{txt}
{com}. file write `hh2' "\item (b) Cutoffs are created based on whether their ability to perform each item at the baseline was higher or lower than the median. " _newline
{txt}
{com}. file write `hh2' "\item (c) Wild cluster bootstrap p-values are reported within parentheses. Clusters are schools at the baseline." _newline
{txt}
{com}. file write `hh2' "\item (d) $^*$ Significant at 10\% level; $^{c -(}**{c )-}$ significant at 5\% level; $^{c -(}***{c )-}$ significant at 1\% level. " _newline
{txt}
{com}. file write `hh2' "\end{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}threeparttable{c )-}" _newline
{txt}
{com}. file write `hh2' "{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:addlabel{c )-}%" _newline
{txt}
{com}. file write `hh2' "\end{c -(}table{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. file close `hh2'
{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/mb/s9qljd594gbfyhl2dqnnwlcw0000gn/T//SD56186.000000"
{txt}
{com}. * This is the do file to create "Table 5. Heterogeneity by Baseline Abilites (Math and CPCS)"
. set seed 1111
{txt}
{com}. 
. use "$path_data/temp/followup_student_baseline_score_missing_dummy", replace
{txt}
{com}. 
. egen DT_score_pre_std_med = median(DT_score_pre_std)
{txt}
{com}. gen DT_score_pre_std_upper50 = 1 if DT_score_pre_std>DT_score_pre_std_med
{txt}(121 missing values generated)

{com}. recode DT_score_pre_std_upper50 (.=0)
{txt}(121 changes made to {bf:DT_score_pre_std_upper50})

{com}. 
. egen rosen_pre_std_med = median(rosen_pre_std)
{txt}
{com}. gen rosen_pre_std_upper50 = 1 if rosen_pre_std>rosen_pre_std_med
{txt}(128 missing values generated)

{com}. recode rosen_pre_std_upper50 (.=0)
{txt}(128 changes made to {bf:rosen_pre_std_upper50})

{com}. rename rosen_pre_std_upper50 RSES_std_upper50
{res}{txt}
{com}. 
. egen cpcs_pre_std_med = median(cpcs_pre_std)
{txt}
{com}. gen cpcs_pre_std_upper50 = 1 if cpcs_pre_std>cpcs_pre_std_med
{txt}(135 missing values generated)

{com}. recode cpcs_pre_std_upper50 (.=0)
{txt}(135 changes made to {bf:cpcs_pre_std_upper50})

{com}. rename cpcs_pre_std_upper50 CPCS_std_upper50
{res}{txt}
{com}. 
. 
. /// Cognitive
> wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 59
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 22
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2397567{col 38}{space 1}  -0.86{col 46}{space 3}0.438{col 54}{space 3}-.8084687{col 66}{space 3} .4069068
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_rses_u = e(N)
{txt}
{com}. scalar n_clust_cog_u_rses_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.3459862{col 38}{space 1}  -1.14{col 46}{space 3}0.242{col 54}{space 3}-1.021292{col 66}{space 3} .3479614
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_rses_l = e(N)
{txt}
{com}. scalar n_clust_cog_u_rses_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2419353{col 38}{space 1}  -0.87{col 46}{space 3}0.458{col 54}{space 3}-.8886186{col 66}{space 3} .4312118
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_rses_u = e(N)
{txt}
{com}. scalar n_clust_cog_l_rses_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0781293{col 38}{space 1}  -0.29{col 46}{space 3}0.790{col 54}{space 3}-.6038098{col 66}{space 3} .5255066
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_rses_l = e(N)
{txt}
{com}. scalar n_clust_cog_l_rses_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 55
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0533319{col 38}{space 1}   0.17{col 46}{space 3}0.842{col 54}{space 3}-.6204316{col 66}{space 3} .8102234
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_cpcs_u = e(N)
{txt}
{com}. scalar n_clust_cog_u_cpcs_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 67
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.5660393{col 38}{space 1}  -1.84{col 46}{space 3}0.090{col 54}{space 3}-1.183374{col 66}{space 3} .1191932
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_cpcs_l = e(N)
{txt}
{com}. scalar n_clust_cog_u_cpcs_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0295186{col 38}{space 1}   0.10{col 46}{space 3}0.884{col 54}{space 3}-.6082701{col 66}{space 3}  .801894
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_cpcs_u = e(N)
{txt}
{com}. scalar n_clust_cog_l_cpcs_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 68
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  -.19243{col 38}{space 1}  -0.73{col 46}{space 3}0.448{col 54}{space 3} -.765462{col 66}{space 3} .3429002
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_cpcs_l = e(N)
{txt}
{com}. scalar n_clust_cog_l_cpcs_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. 
. 
. /// RSES
> wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .9579135{col 38}{space 1}   3.36{col 46}{space 3}0.010{col 54}{space 3} .2787565{col 66}{space 3}  1.62041
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_u_cog_u = e(N)
{txt}
{com}. scalar n_clust_rses_u_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 61
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0615952{col 38}{space 1}   0.32{col 46}{space 3}0.766{col 54}{space 3} -.397067{col 66}{space 3} .4088516
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_l_cog_u = e(N)
{txt}
{com}. scalar n_clust_rses_l_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .4920182{col 38}{space 1}   1.44{col 46}{space 3}0.194{col 54}{space 3}-.2628764{col 66}{space 3} 1.175857
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_u_cog_l = e(N)
{txt}
{com}. scalar n_clust_rses_u_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1388312{col 38}{space 1}   0.42{col 46}{space 3}0.624{col 54}{space 3}-.5883907{col 66}{space 3} .8934127
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_l_cog_l = e(N)
{txt}
{com}. scalar n_clust_rses_l_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 52
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 20
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .6664341{col 38}{space 1}   3.26{col 46}{space 3}0.002{col 54}{space 3} .2055946{col 66}{space 3}  1.14356
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3847293{col 38}{space 1}   1.53{col 46}{space 3}0.146{col 54}{space 3}-.1643196{col 66}{space 3} .9509508
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2062219{col 38}{space 1}   0.47{col 46}{space 3}0.602{col 54}{space 3}-.8201526{col 66}{space 3} 1.180365
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 66
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3332931{col 38}{space 1}   0.96{col 46}{space 3}0.410{col 54}{space 3}-.4739231{col 66}{space 3} 1.063097
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. 
. 
. /// CPCS
> 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} 1.020132{col 38}{space 1}   3.66{col 46}{space 3}0.002{col 54}{space 3} .4189367{col 66}{space 3} 1.690029
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 61
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0665639{col 38}{space 1}   0.34{col 46}{space 3}0.742{col 54}{space 3}-.3967434{col 66}{space 3} .5061282
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .5458399{col 38}{space 1}   1.63{col 46}{space 3}0.150{col 54}{space 3}-.2496979{col 66}{space 3} 1.250735
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1431476{col 38}{space 1}   0.41{col 46}{space 3}0.704{col 54}{space 3}-.7431742{col 66}{space 3} .9775981
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 52
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 20
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  .774167{col 38}{space 1}   3.68{col 46}{space 3}0.002{col 54}{space 3} .3336309{col 66}{space 3} 1.253487
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_u_cog_u = e(N)
{txt}
{com}. scalar n_clust_cpcs_u_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3575321{col 38}{space 1}   1.51{col 46}{space 3}0.142{col 54}{space 3}-.1495744{col 66}{space 3} .9198072
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_l_cog_u = e(N)
{txt}
{com}. scalar n_clust_cpcs_l_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2792052{col 38}{space 1}   0.63{col 46}{space 3}0.568{col 54}{space 3}-.8609089{col 66}{space 3} 1.260092
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_u_cog_l = e(N)
{txt}
{com}. scalar n_clust_cpcs_u_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 66
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}   .33148{col 38}{space 1}   0.95{col 46}{space 3}0.418{col 54}{space 3}-.4964017{col 66}{space 3} 1.053111
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_l_cog_l = e(N)
{txt}
{com}. scalar n_clust_cpcs_l_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. // significant level
. 
. 
. local outcome cog rses cpcs
{txt}
{com}. local hetero1 cogU cogL
{txt}
{com}. local hetero2 rsesU rsesL
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. foreach h1 in `hetero1'{c -(}
{txt}  3{com}. foreach h2 in `hetero2'{c -(}
{txt}  4{com}.                 if `dep'_`h1'_`h2'_pv[1,1]<=0.01 {c -(}
{txt}  5{com}.                         local star_`dep'_`h1'_`h2' %3s "***"
{txt}  6{com}.                 {c )-}
{txt}  7{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.01) & (`dep'_`h1'_`h2'_pv[1,1]<=0.05) {c -(}
{txt}  8{com}.                         local star_`dep'_`h1'_`h2' %2s "**"
{txt}  9{com}.                 {c )-}
{txt} 10{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.05) & (`dep'_`h1'_`h2'_pv[1,1]<=0.10) {c -(}
{txt} 11{com}.                         local star_`dep'_`h1'_`h2' %1s "*"
{txt} 12{com}.                 {c )-}
{txt} 13{com}.                 else {c -(}
{txt} 14{com}.                         local star_`dep'_`h1'_`h2'  ""
{txt} 15{com}.                 {c )-}
{txt} 16{com}. {c )-} 
{txt} 17{com}. {c )-}
{txt} 18{com}. {c )-}
{txt}
{com}. 
. local outcome cog rses cpcs
{txt}
{com}. local hetero1 cogU cogL
{txt}
{com}. local hetero2 cpcsU cpcsL
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. foreach h1 in `hetero1'{c -(}
{txt}  3{com}. foreach h2 in `hetero2'{c -(}
{txt}  4{com}.                 if `dep'_`h1'_`h2'_pv[1,1]<=0.01 {c -(}
{txt}  5{com}.                         local star_`dep'_`h1'_`h2' %3s "***"
{txt}  6{com}.                 {c )-}
{txt}  7{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.01) & (`dep'_`h1'_`h2'_pv[1,1]<=0.05) {c -(}
{txt}  8{com}.                         local star_`dep'_`h1'_`h2' %2s "**"
{txt}  9{com}.                 {c )-}
{txt} 10{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.05) & (`dep'_`h1'_`h2'_pv[1,1]<=0.10) {c -(}
{txt} 11{com}.                         local star_`dep'_`h1'_`h2' %1s "*"
{txt} 12{com}.                 {c )-}
{txt} 13{com}.                 else {c -(}
{txt} 14{com}.                         local star_`dep'_`h1'_`h2'  ""
{txt} 15{com}.                 {c )-}
{txt} 16{com}. {c )-} 
{txt} 17{com}. {c )-}
{txt} 18{com}. {c )-}
{txt}
{com}. 
. /// Table
> 
. tempname hh2
{txt}
{com}. file open `hh2' using "$path_output/hetero_2by2_CPCS.tex", write replace
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "% Author: Kazuma Takakura" _newline
{txt}
{com}. file write `hh2' "% Date: `c(current_date)'" _newline
{txt}
{com}. file write `hh2' "% Time: `c(current_time)'" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. 
. file write `hh2' "\begin{c -(}table{c )-}[h!]\footnotesize" _newline
{txt}
{com}. file write `hh2' "  \centering" _newline
{txt}
{com}. file write `hh2' "  \caption{c -(}Heterogeneity among Baseline Abilites (Math and CPCS){c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:hetero_cpcs{c )-}" _newline
{txt}
{com}. file write `hh2' "\scalebox{c -(}1{c )-}{c -(}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}threeparttable{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "\begin{c -(}tabular{c )-}{c -(}lccccccc{c )-}\toprule" _newline
{txt}
{com}. 
. file write `hh2' " Dependent Variable & \multicolumn{c -(}2{c )-}{c -(}c{c )-}{c -(}Baseline^{c -(}b{c )-}{c )-} & Difference & Obs & N of clusters  \\\midrule\midrule" _newline
{txt}
{com}. file write `hh2' " Rapid math test score^{c -(}a{c )-} & Math Top 50\%  & CPCS Top 50\% & " %04.3f (cog_cogU_rsesU_mean[1,1]) `star_cog_cogU_cpcsU' " & " %02.0f ( n_cog_u_cpcs_u ) " & " %02.0f ( n_clust_cog_u_cpcs_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogU_cpcsU_pv[1,1]) " )  & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cog_cogU_cpcsL_mean[1,1]) `star_cog_cogU_cpcsL' " & " %02.0f ( n_cog_u_cpcs_l ) " & " %02.0f ( n_clust_cog_u_cpcs_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogU_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & Math Bottom 50\%  & CPCS Top 50\% & " %04.3f (cog_cogL_cpcsU_mean[1,1]) `star_cog_cogL_cpcsU' " & " %02.0f ( n_cog_l_cpcs_u ) " & " %02.0f ( n_clust_cog_l_cpcs_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogL_cpcsU_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cog_cogL_cpcsL_mean[1,1]) `star_cog_cogL_cpcsL' " & " %02.0f ( n_cog_l_cpcs_l ) " & " %02.0f ( n_clust_cog_l_cpcs_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogL_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. 
. file write `hh2' " CPCS score^{c -(}a{c )-} & Math Top 50\%  & CPCS Top 50\% & " %04.3f (cpcs_cogU_cpcsU_mean[1,1]) `star_cpcs_cogU_cpcsU' " & " %02.0f ( n_cpcs_u_cog_u ) " & " %02.0f ( n_clust_cpcs_u_cog_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cpcs_cogU_cpcsU_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cpcs_cogU_cpcsL_mean[1,1]) `star_cpcs_cogU_cpcsL' " & " %02.0f ( n_cpcs_l_cog_u ) " & " %02.0f ( n_clust_cpcs_l_cog_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &       & & ( " %04.3f (cpcs_cogU_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & Math Bottom 50\%  & CPCS Top 50\% & " %04.3f (cpcs_cogL_cpcsU_mean[1,1]) `star_cpcs_cogL_cpcsU' " & " %02.0f ( n_cpcs_u_cog_l ) " & " %02.0f ( n_clust_cpcs_u_cog_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cpcs_cogL_cpcsU_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cpcs_cogL_cpcsL_mean[1,1]) `star_cpcs_cogL_cpcsL' " & " %02.0f ( n_cpcs_l_cog_l ) " & " %02.0f ( n_clust_cpcs_l_cog_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &       & & ( " %04.3f (cpcs_cogL_cpcsL_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. 
. file write `hh2' "\\\midrule" _newline
{txt}
{com}. file write `hh2' "\end{c -(}tabular{c )-}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\item (a) Dependent variables are standardized using the average and variance of the whole follow-up sample in the February 2022 survey. " _newline
{txt}
{com}. file write `hh2' "\item (b) Cutoffs are created based on whether their ability to perform each item at the baseline was higher or lower than the median. " _newline
{txt}
{com}. file write `hh2' "\item (c) Wild cluster bootstrap p-values are reported within parentheses. Clusters are schools at the baseline." _newline
{txt}
{com}. file write `hh2' "\item (d) $^*$ Significant at 10\% level; $^{c -(}**{c )-}$ significant at 5\% level; $^{c -(}***{c )-}$ significant at 1\% level. " _newline
{txt}
{com}. file write `hh2' "\end{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}threeparttable{c )-}" _newline
{txt}
{com}. file write `hh2' "{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:addlabel{c )-}%" _newline
{txt}
{com}. file write `hh2' "\end{c -(}table{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. file close `hh2'
{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/mb/s9qljd594gbfyhl2dqnnwlcw0000gn/T//SD56186.000000"
{txt}
{com}. * This is the do file to create "Table 5. Heterogeneity by Baseline Abilites (Math and CPCS)"
. set seed 11111
{txt}
{com}. 
. use "$path_data/temp/followup_student_baseline_score_missing_dummy", replace
{txt}
{com}. 
. egen DT_score_pre_std_med = median(DT_score_pre_std)
{txt}
{com}. gen DT_score_pre_std_upper50 = 1 if DT_score_pre_std>DT_score_pre_std_med
{txt}(121 missing values generated)

{com}. recode DT_score_pre_std_upper50 (.=0)
{txt}(121 changes made to {bf:DT_score_pre_std_upper50})

{com}. 
. egen rosen_pre_std_med = median(rosen_pre_std)
{txt}
{com}. gen rosen_pre_std_upper50 = 1 if rosen_pre_std>rosen_pre_std_med
{txt}(128 missing values generated)

{com}. recode rosen_pre_std_upper50 (.=0)
{txt}(128 changes made to {bf:rosen_pre_std_upper50})

{com}. rename rosen_pre_std_upper50 RSES_std_upper50
{res}{txt}
{com}. 
. egen cpcs_pre_std_med = median(cpcs_pre_std)
{txt}
{com}. gen cpcs_pre_std_upper50 = 1 if cpcs_pre_std>cpcs_pre_std_med
{txt}(135 missing values generated)

{com}. recode cpcs_pre_std_upper50 (.=0)
{txt}(135 changes made to {bf:cpcs_pre_std_upper50})

{com}. rename cpcs_pre_std_upper50 CPCS_std_upper50
{res}{txt}
{com}. 
. 
. /// Cognitive
> wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 59
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 22
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2397567{col 38}{space 1}  -0.86{col 46}{space 3}0.442{col 54}{space 3}-.8406214{col 66}{space 3} .4161941
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_rses_u = e(N)
{txt}
{com}. scalar n_clust_cog_u_rses_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.3459862{col 38}{space 1}  -1.14{col 46}{space 3}0.272{col 54}{space 3}-.9390312{col 66}{space 3} .2653954
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_rses_l = e(N)
{txt}
{com}. scalar n_clust_cog_u_rses_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2419353{col 38}{space 1}  -0.87{col 46}{space 3}0.376{col 54}{space 3}-.8813633{col 66}{space 3} .3970286
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_rses_u = e(N)
{txt}
{com}. scalar n_clust_cog_l_rses_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0781293{col 38}{space 1}  -0.29{col 46}{space 3}0.782{col 54}{space 3} -.633589{col 66}{space 3} .5514957
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_rses_l = e(N)
{txt}
{com}. scalar n_clust_cog_l_rses_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 55
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0533319{col 38}{space 1}   0.17{col 46}{space 3}0.882{col 54}{space 3}-.6611625{col 66}{space 3}  .806006
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_cpcs_u = e(N)
{txt}
{com}. scalar n_clust_cog_u_cpcs_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 67
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.5660393{col 38}{space 1}  -1.84{col 46}{space 3}0.106{col 54}{space 3}-1.193385{col 66}{space 3} .1454137
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_cpcs_l = e(N)
{txt}
{com}. scalar n_clust_cog_u_cpcs_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0295186{col 38}{space 1}   0.10{col 46}{space 3}0.894{col 54}{space 3}-.5566965{col 66}{space 3} .7313526
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_cpcs_u = e(N)
{txt}
{com}. scalar n_clust_cog_l_cpcs_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 68
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  -.19243{col 38}{space 1}  -0.73{col 46}{space 3}0.530{col 54}{space 3}-.7624104{col 66}{space 3} .3217706
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_cpcs_l = e(N)
{txt}
{com}. scalar n_clust_cog_l_cpcs_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. 
. 
. /// RSES
> wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .9579135{col 38}{space 1}   3.36{col 46}{space 3}0.006{col 54}{space 3} .2995461{col 66}{space 3}  1.61595
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_u_cog_u = e(N)
{txt}
{com}. scalar n_clust_rses_u_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 61
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0615952{col 38}{space 1}   0.32{col 46}{space 3}0.772{col 54}{space 3}-.3396273{col 66}{space 3} .4674283
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_l_cog_u = e(N)
{txt}
{com}. scalar n_clust_rses_l_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .4920182{col 38}{space 1}   1.44{col 46}{space 3}0.196{col 54}{space 3}-.3089123{col 66}{space 3} 1.207425
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_u_cog_l = e(N)
{txt}
{com}. scalar n_clust_rses_u_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1388312{col 38}{space 1}   0.42{col 46}{space 3}0.754{col 54}{space 3}-.6694186{col 66}{space 3}   .96379
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_l_cog_l = e(N)
{txt}
{com}. scalar n_clust_rses_l_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 52
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 20
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .6664341{col 38}{space 1}   3.26{col 46}{space 3}0.006{col 54}{space 3} .1980499{col 66}{space 3} 1.114933
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3847293{col 38}{space 1}   1.53{col 46}{space 3}0.132{col 54}{space 3} -.142619{col 66}{space 3} .9763969
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2062219{col 38}{space 1}   0.47{col 46}{space 3}0.648{col 54}{space 3}-.9251009{col 66}{space 3} 1.214517
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 66
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3332931{col 38}{space 1}   0.96{col 46}{space 3}0.396{col 54}{space 3}-.5699815{col 66}{space 3}  1.19557
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. 
. 
. /// CPCS
> 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} 1.020132{col 38}{space 1}   3.66{col 46}{space 3}0.006{col 54}{space 3} .4028772{col 66}{space 3}  1.72851
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 61
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0665639{col 38}{space 1}   0.34{col 46}{space 3}0.684{col 54}{space 3} -.332363{col 66}{space 3} .4531203
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:19})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .5458399{col 38}{space 1}   1.63{col 46}{space 3}0.150{col 54}{space 3}-.3241352{col 66}{space 3} 1.232792
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1431476{col 38}{space 1}   0.41{col 46}{space 3}0.738{col 54}{space 3}-.6865997{col 66}{space 3} 1.002436
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 52
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 20
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  .774167{col 38}{space 1}   3.68{col 46}{space 3}0.002{col 54}{space 3} .3584126{col 66}{space 3} 1.254922
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_u_cog_u = e(N)
{txt}
{com}. scalar n_clust_cpcs_u_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3575321{col 38}{space 1}   1.51{col 46}{space 3}0.148{col 54}{space 3}-.1602723{col 66}{space 3}  .881994
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_l_cog_u = e(N)
{txt}
{com}. scalar n_clust_cpcs_l_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2792052{col 38}{space 1}   0.63{col 46}{space 3}0.514{col 54}{space 3}-.8447186{col 66}{space 3} 1.281683
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_u_cog_l = e(N)
{txt}
{com}. scalar n_clust_cpcs_u_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 66
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}   .33148{col 38}{space 1}   0.95{col 46}{space 3}0.408{col 54}{space 3}-.4831461{col 66}{space 3} 1.121361
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_l_cog_l = e(N)
{txt}
{com}. scalar n_clust_cpcs_l_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. // significant level
. 
. 
. local outcome cog rses cpcs
{txt}
{com}. local hetero1 cogU cogL
{txt}
{com}. local hetero2 rsesU rsesL
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. foreach h1 in `hetero1'{c -(}
{txt}  3{com}. foreach h2 in `hetero2'{c -(}
{txt}  4{com}.                 if `dep'_`h1'_`h2'_pv[1,1]<=0.01 {c -(}
{txt}  5{com}.                         local star_`dep'_`h1'_`h2' %3s "***"
{txt}  6{com}.                 {c )-}
{txt}  7{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.01) & (`dep'_`h1'_`h2'_pv[1,1]<=0.05) {c -(}
{txt}  8{com}.                         local star_`dep'_`h1'_`h2' %2s "**"
{txt}  9{com}.                 {c )-}
{txt} 10{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.05) & (`dep'_`h1'_`h2'_pv[1,1]<=0.10) {c -(}
{txt} 11{com}.                         local star_`dep'_`h1'_`h2' %1s "*"
{txt} 12{com}.                 {c )-}
{txt} 13{com}.                 else {c -(}
{txt} 14{com}.                         local star_`dep'_`h1'_`h2'  ""
{txt} 15{com}.                 {c )-}
{txt} 16{com}. {c )-} 
{txt} 17{com}. {c )-}
{txt} 18{com}. {c )-}
{txt}
{com}. 
. local outcome cog rses cpcs
{txt}
{com}. local hetero1 cogU cogL
{txt}
{com}. local hetero2 cpcsU cpcsL
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. foreach h1 in `hetero1'{c -(}
{txt}  3{com}. foreach h2 in `hetero2'{c -(}
{txt}  4{com}.                 if `dep'_`h1'_`h2'_pv[1,1]<=0.01 {c -(}
{txt}  5{com}.                         local star_`dep'_`h1'_`h2' %3s "***"
{txt}  6{com}.                 {c )-}
{txt}  7{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.01) & (`dep'_`h1'_`h2'_pv[1,1]<=0.05) {c -(}
{txt}  8{com}.                         local star_`dep'_`h1'_`h2' %2s "**"
{txt}  9{com}.                 {c )-}
{txt} 10{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.05) & (`dep'_`h1'_`h2'_pv[1,1]<=0.10) {c -(}
{txt} 11{com}.                         local star_`dep'_`h1'_`h2' %1s "*"
{txt} 12{com}.                 {c )-}
{txt} 13{com}.                 else {c -(}
{txt} 14{com}.                         local star_`dep'_`h1'_`h2'  ""
{txt} 15{com}.                 {c )-}
{txt} 16{com}. {c )-} 
{txt} 17{com}. {c )-}
{txt} 18{com}. {c )-}
{txt}
{com}. 
. /// Table
> 
. tempname hh2
{txt}
{com}. file open `hh2' using "$path_output/hetero_2by2_CPCS.tex", write replace
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "% Author: Kazuma Takakura" _newline
{txt}
{com}. file write `hh2' "% Date: `c(current_date)'" _newline
{txt}
{com}. file write `hh2' "% Time: `c(current_time)'" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. 
. file write `hh2' "\begin{c -(}table{c )-}[h!]\footnotesize" _newline
{txt}
{com}. file write `hh2' "  \centering" _newline
{txt}
{com}. file write `hh2' "  \caption{c -(}Heterogeneity among Baseline Abilites (Math and CPCS){c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:hetero_cpcs{c )-}" _newline
{txt}
{com}. file write `hh2' "\scalebox{c -(}1{c )-}{c -(}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}threeparttable{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "\begin{c -(}tabular{c )-}{c -(}lccccccc{c )-}\toprule" _newline
{txt}
{com}. 
. file write `hh2' " Dependent Variable & \multicolumn{c -(}2{c )-}{c -(}c{c )-}{c -(}Baseline^{c -(}b{c )-}{c )-} & Difference & Obs & N of clusters  \\\midrule\midrule" _newline
{txt}
{com}. file write `hh2' " Rapid math test score^{c -(}a{c )-} & Math Top 50\%  & CPCS Top 50\% & " %04.3f (cog_cogU_rsesU_mean[1,1]) `star_cog_cogU_cpcsU' " & " %02.0f ( n_cog_u_cpcs_u ) " & " %02.0f ( n_clust_cog_u_cpcs_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogU_cpcsU_pv[1,1]) " )  & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cog_cogU_cpcsL_mean[1,1]) `star_cog_cogU_cpcsL' " & " %02.0f ( n_cog_u_cpcs_l ) " & " %02.0f ( n_clust_cog_u_cpcs_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogU_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & Math Bottom 50\%  & CPCS Top 50\% & " %04.3f (cog_cogL_cpcsU_mean[1,1]) `star_cog_cogL_cpcsU' " & " %02.0f ( n_cog_l_cpcs_u ) " & " %02.0f ( n_clust_cog_l_cpcs_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogL_cpcsU_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cog_cogL_cpcsL_mean[1,1]) `star_cog_cogL_cpcsL' " & " %02.0f ( n_cog_l_cpcs_l ) " & " %02.0f ( n_clust_cog_l_cpcs_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogL_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. 
. file write `hh2' " CPCS score^{c -(}a{c )-} & Math Top 50\%  & CPCS Top 50\% & " %04.3f (cpcs_cogU_cpcsU_mean[1,1]) `star_cpcs_cogU_cpcsU' " & " %02.0f ( n_cpcs_u_cog_u ) " & " %02.0f ( n_clust_cpcs_u_cog_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cpcs_cogU_cpcsU_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cpcs_cogU_cpcsL_mean[1,1]) `star_cpcs_cogU_cpcsL' " & " %02.0f ( n_cpcs_l_cog_u ) " & " %02.0f ( n_clust_cpcs_l_cog_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &       & & ( " %04.3f (cpcs_cogU_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & Math Bottom 50\%  & CPCS Top 50\% & " %04.3f (cpcs_cogL_cpcsU_mean[1,1]) `star_cpcs_cogL_cpcsU' " & " %02.0f ( n_cpcs_u_cog_l ) " & " %02.0f ( n_clust_cpcs_u_cog_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cpcs_cogL_cpcsU_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cpcs_cogL_cpcsL_mean[1,1]) `star_cpcs_cogL_cpcsL' " & " %02.0f ( n_cpcs_l_cog_l ) " & " %02.0f ( n_clust_cpcs_l_cog_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &       & & ( " %04.3f (cpcs_cogL_cpcsL_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. 
. file write `hh2' "\\\midrule" _newline
{txt}
{com}. file write `hh2' "\end{c -(}tabular{c )-}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\item (a) Dependent variables are standardized using the average and variance of the whole follow-up sample in the February 2022 survey. " _newline
{txt}
{com}. file write `hh2' "\item (b) Cutoffs are created based on whether their ability to perform each item at the baseline was higher or lower than the median. " _newline
{txt}
{com}. file write `hh2' "\item (c) Wild cluster bootstrap p-values are reported within parentheses. Clusters are schools at the baseline." _newline
{txt}
{com}. file write `hh2' "\item (d) $^*$ Significant at 10\% level; $^{c -(}**{c )-}$ significant at 5\% level; $^{c -(}***{c )-}$ significant at 1\% level. " _newline
{txt}
{com}. file write `hh2' "\end{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}threeparttable{c )-}" _newline
{txt}
{com}. file write `hh2' "{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:addlabel{c )-}%" _newline
{txt}
{com}. file write `hh2' "\end{c -(}table{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. file close `hh2'
{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/mb/s9qljd594gbfyhl2dqnnwlcw0000gn/T//SD56186.000000"
{txt}
{com}. * This is the do file to create "Table 1. Summary Statistics"
. set seed 11111
{txt}
{com}. 
. use "$path_data/temp/followup_student_parents_matched", clear
{txt}
{com}. 
. corr rosen_pre_std cpcs_pre_std
{txt}(obs=243)

             {c |} rosen_~d cpcs_p~d
{hline 13}{c +}{hline 18}
rosen_pre_~d {c |}{res}   1.0000
{txt}cpcs_pre_std {c |}{res}   0.9026   1.0000

{txt}
{com}. corr RSES_std CPCS_std
{txt}(obs=236)

             {c |} RSES_std CPCS_std
{hline 13}{c +}{hline 18}
    RSES_std {c |}{res}   1.0000
    {txt}CPCS_std {c |}{res}   0.9701   1.0000

{txt}
{com}. 
. 
. /// Varable Selection
> /// Baseline
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat DT_score_pre_std rosen_pre_std cpcs_pre_std if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_bl = r(StatTotal)
{txt}  5{com}. 
. tabstat DT_score_pre_std rosen_pre_std cpcs_pre_std if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_bl = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}      144       145       145
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   DT_score_p~d  rosen_pre_~d  cpcs_pre_std
N {res}          144           145           145
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}       95        98        98
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   DT_score_p~d  rosen_pre_~d  cpcs_pre_std
N {res}           95            98            98
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res}-.0313509  .0382918  .1345164
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      DT_score_p~d  rosen_pre_~d  cpcs_pre_std
Mean {res}   -.03135095     .03829184      .1345164
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .0475214 -.0566567 -.1990291
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      DT_score_p~d  rosen_pre_~d  cpcs_pre_std
Mean {res}    .04752144    -.05665667    -.19902912
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} 1.023177  .9748496  .9271749
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    DT_score_p~d  rosen_pre_~d  cpcs_pre_std
SD {res}    1.0231772     .97484957     .92717486
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} .9672202  1.038561  1.073121
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    DT_score_p~d  rosen_pre_~d  cpcs_pre_std
SD {res}    .96722024      1.038561     1.0731214
{reset}
{com}. 
. matrix n_bl = J(1,3,.)
{txt}
{com}. forvalues i = 1/3 {c -(}
{txt}  2{com}.         matrix n_bl[1,`i'] = n_tr_bl[1,`i'] + n_ct_bl[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in DT_score_pre_std rosen_pre_std cpcs_pre_std{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}239
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 32
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  2
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.5
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        DT_score_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0788724{col 38}{space 1}  -0.38{col 46}{space 3}0.734{col 54}{space 3}-.5189742{col 66}{space 3} .3696684
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}           rosen_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0949485{col 38}{space 1}   0.47{col 46}{space 3}0.646{col 54}{space 3}-.3607952{col 66}{space 3} .5030062
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}            cpcs_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3335455{col 38}{space 1}   1.82{col 46}{space 3}0.100{col 54}{space 3}-.0582149{col 66}{space 3} .7601597
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. /// Family
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat hhmember hhheadage hhheadeduyear if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_parent = r(StatTotal)
{txt}  5{com}. 
. tabstat hhmember hhheadage hhheadeduyear if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_parent = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}      145       145       145
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
       hhmember     hhheadage  hhheadeduy~r
N {res}          145           145           145
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}       98        98        98
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
       hhmember     hhheadage  hhheadeduy~r
N {res}           98            98            98
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res} 4.510345  46.57241  2.331034
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
          hhmember     hhheadage  hhheadeduy~r
Mean {res}    4.5103448     46.572414     2.3310345
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res} 4.265306  46.68878  3.163265
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
          hhmember     hhheadage  hhheadeduy~r
Mean {res}    4.2653061     46.688776     3.1632653
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} 1.280827   9.03907  2.995495
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
        hhmember     hhheadage  hhheadeduy~r
SD {res}    1.2808268     9.0390702     2.9954947
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} 1.197515  9.408681  3.530993
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
        hhmember     hhheadage  hhheadeduy~r
SD {res}    1.1975148     9.4086808     3.5309935
{reset}
{com}. 
. matrix n_parent = J(1,3,.)
{txt}
{com}. forvalues i = 1/3 {c -(}
{txt}  2{com}.         matrix n_parent[1,`i'] = n_tr_parent[1,`i'] + n_ct_parent[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in hhmember hhheadage hhheadeduyear{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}30{text} done{text} ({result:30})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                hhmember{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2450387{col 38}{space 1}   1.29{col 46}{space 3}0.194{col 54}{space 3}-.1417022{col 66}{space 3} .6348151
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}               hhheadage{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.1163617{col 38}{space 1}  -0.07{col 46}{space 3}0.922{col 54}{space 3}-3.505462{col 66}{space 3}  3.15497
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}           hhheadeduyear{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.8322308{col 38}{space 1}  -2.22{col 46}{space 3}0.040{col 54}{space 3}-1.643988{col 66}{space 3}-.0853419
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. 
. 
. /// School　attendance
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat q2a q2b q2c q2h if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_school = r(StatTotal)
{txt}  5{com}. 
. tabstat q2a q2b q2c q2h if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_school = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:N} {...}
{c |}{...}
 {res}      145       145       145       145
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
   q2a  q2b  q2c  q2h
N {res} 145  145  145  145
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:N} {...}
{c |}{...}
 {res}       98        98        98        98
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
   q2a  q2b  q2c  q2h
N {res}  98   98   98   98
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .5517241  9.606897   .062069  .3793103
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
            q2a        q2b        q2c        q2h
Mean {res} .55172414  9.6068966  .06206897  .37931034
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .5306122  9.602041  .0408163  .4489796
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
            q2a        q2b        q2c        q2h
Mean {res} .53061224  9.6020408  .04081633  .44897959
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:SD} {...}
{c |}{...}
 {res} .4990412  1.029405  .2421171  .4868973
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
          q2a        q2b        q2c        q2h
SD {res} .49904123  1.0294048   .2421171  .48689728
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:SD} {...}
{c |}{...}
 {res} .5016279  .8703571  .1988818  .4999474
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
          q2a        q2b        q2c        q2h
SD {res}  .5016279  .87035715  .19888179   .4999474
{reset}
{com}. 
. matrix n_school = J(1,4,.)
{txt}
{com}. forvalues i = 1/4 {c -(}
{txt}  2{com}.         matrix n_school[1,`i'] = n_tr_school[1,`i'] + n_ct_school[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in q2a q2b q2c q2h{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                     q2a{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0211119{col 38}{space 1}   0.25{col 46}{space 3}0.820{col 54}{space 3}-.1489611{col 66}{space 3} .2042166
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                     q2b{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0048557{col 38}{space 1}   0.03{col 46}{space 3}0.946{col 54}{space 3} -.365202{col 66}{space 3} .3892254
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                     q2c{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0212526{col 38}{space 1}   0.56{col 46}{space 3}0.658{col 54}{space 3}-.0535988{col 66}{space 3} .0976725
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                     q2h{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0696692{col 38}{space 1}  -0.85{col 46}{space 3}0.386{col 54}{space 3}-.2456035{col 66}{space 3}  .103558
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. /// Other study variable
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat tutor study_other study_affect_covid hometutoring onlineclass studymyself parentsteach if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_study = r(StatTotal)
{txt}  5{com}. 
. tabstat tutor study_other study_affect_covid hometutoring onlineclass studymyself parentsteach if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_study = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:N} {...}
{c |}{...}
 {res}      145       145       145       145       145       145       145
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
          tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
N {res}          145           145           145           145           145           145           145
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:N} {...}
{c |}{...}
 {res}       98        98        98        98        98        98        98
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
          tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
N {res}           98            98            98            98            98            98            98
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:Mean} {...}
{c |}{...}
 {res}  .337931   .462069  .6482759  .0965517  .0482759  .5241379  .0275862
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
             tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
Mean {res}    .33793103     .46206897     .64827586     .09655172     .04827586     .52413793     .02758621
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .4591837  .4285714  .6020408  .1428571  .1326531  .5306122  .1938776
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
             tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
Mean {res}    .45918367     .42857143     .60204082     .14285714     .13265306     .53061224     .19387755
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:SD} {...}
{c |}{...}
 {res} .4746445  .5002873  .4791635  .2963701  .2150915  .5011481  .1643517
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
           tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
SD {res}    .47464445     .50028727     .47916354     .29637012     .21509153     .50114811     .16435174
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:SD} {...}
{c |}{...}
 {res} .5008934   .497416  .4919935  .3517262  .3409434  .5016279  .3973667
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
           tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
SD {res}    .50089337       .497416     .49199354     .35172623     .34094336      .5016279     .39736667
{reset}
{com}. 
. matrix n_study = J(1,8,.)
{txt}
{com}. forvalues i = 1/8 {c -(}
{txt}  2{com}.         matrix n_study[1,`i'] = n_tr_study[1,`i'] + n_ct_study[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in tutor study_other study_affect_covid hometutoring onlineclass studymyself parentsteach{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                   tutor{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.1212526{col 38}{space 1}  -1.69{col 46}{space 3}0.124{col 54}{space 3}-.2711766{col 66}{space 3} .0377352
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}             study_other{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0334975{col 38}{space 1}   0.39{col 46}{space 3}0.690{col 54}{space 3}-.1459961{col 66}{space 3} .2463719
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}      study_affect_covid{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  .046235{col 38}{space 1}   0.56{col 46}{space 3}0.564{col 54}{space 3}-.1428084{col 66}{space 3} .2111943
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:19})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}            hometutoring{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0463054{col 38}{space 1}  -1.11{col 46}{space 3}0.262{col 54}{space 3}-.1310113{col 66}{space 3} .0317248
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}             onlineclass{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0843772{col 38}{space 1}  -1.92{col 46}{space 3}0.068{col 54}{space 3}-.1803838{col 66}{space 3} .0054597
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}             studymyself{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0064743{col 38}{space 1}  -0.08{col 46}{space 3}0.906{col 54}{space 3}-.1836318{col 66}{space 3} .1622524
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}            parentsteach{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.1662913{col 38}{space 1}  -3.85{col 46}{space 3}0.004{col 54}{space 3}-.2565448{col 66}{space 3}-.0564223
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. /// Cognitive
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat followup_cog_std if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_cog = r(StatTotal)
{txt}  5{com}. 
. tabstat followup_cog_std if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_cog = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}{ralign 12:Variable} {...}
{c |}         N
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res}      145
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
   followup_c~d
N {res}          145
{reset}
{ralign 12:Variable} {...}
{c |}         N
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res}       98
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
   followup_c~d
N {res}           98
{reset}
{ralign 12:Variable} {...}
{c |}      Mean
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res}-.0920409
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
      followup_c~d
Mean {res}   -.09204085
{reset}
{ralign 12:Variable} {...}
{c |}      Mean
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res} .1361831
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
      followup_c~d
Mean {res}    .13618309
{reset}
{ralign 12:Variable} {...}
{c |}        SD
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res} 1.070796
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
    followup_c~d
SD {res}     1.070796
{reset}
{ralign 12:Variable} {...}
{c |}        SD
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res} .8725076
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
    followup_c~d
SD {res}    .87250763
{reset}
{com}. 
. matrix n_cog = J(1,1,.)
{txt}
{com}. forvalues i = 1/1 {c -(}
{txt}  2{com}.         matrix n_cog[1,`i'] = n_tr_cog[1,`i'] + n_ct_cog[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2282239{col 38}{space 1}  -1.36{col 46}{space 3}0.152{col 54}{space 3}-.5916805{col 66}{space 3} .1235634
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}.         
. matrix r2_followup_cog_std_temp = r(table)
{txt}
{com}. 
. 
. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix r2_followup_cog_std_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix r2_followup_cog_std_mean[1,`j'] = r2_followup_cog_std_temp[1,`j']
{txt}  3{com}. * standard error
. * matrix r2_followup_cog_std_se[1,`j'] = r2_followup_cog_std_temp[2,`j']
. * p value
. matrix r2_followup_cog_std_pv[1,`j'] = r2_followup_cog_std_temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}.     
. /// Non cognitive
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat followup_noncog_std RSES_std CPCS_std if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_noncog = r(StatTotal)
{txt}  5{com}. 
. tabstat followup_noncog_std RSES_std CPCS_std if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_noncog = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}      105       140       140
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   followup_n~d      RSES_std      CPCS_std
N {res}          105           140           140
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}       74        96        96
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   followup_n~d      RSES_std      CPCS_std
N {res}           74            96            96
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .1969319  .1591241  .1745941
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      followup_n~d      RSES_std      CPCS_std
Mean {res}    .19693189      .1591241     .17459415
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res}-.2794302 -.2320565  -.254617
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      followup_n~d      RSES_std      CPCS_std
Mean {res}   -.27943024    -.23205648    -.25461705
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} 1.006158  1.022691  1.008304
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    followup_n~d      RSES_std      CPCS_std
SD {res}    1.0061577     1.0226907     1.0083041
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} .9279901  .9228443  .9357831
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    followup_n~d      RSES_std      CPCS_std
SD {res}    .92799012     .92284427     .93578307
{reset}
{com}. 
. matrix n_noncog = J(1,3,.)
{txt}
{com}. forvalues i = 1/3 {c -(}
{txt}  2{com}.         matrix n_noncog[1,`i'] = n_tr_noncog[1,`i'] + n_ct_noncog[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in followup_noncog_std RSES_std CPCS_std{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}30{text}.{text} done{text} ({result:31})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}179
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 32
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}5.6
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}     followup_noncog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .4763621{col 38}{space 1}   2.08{col 46}{space 3}0.070{col 54}{space 3}-.0151593{col 66}{space 3} .9700348
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}30{text} done{text} ({result:30})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}236
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.2
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3911806{col 38}{space 1}   2.02{col 46}{space 3}0.058{col 54}{space 3}-.0075009{col 66}{space 3} .8044327
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:29})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}236
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.2
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .4292112{col 38}{space 1}   2.26{col 46}{space 3}0.026{col 54}{space 3}  .044848{col 66}{space 3} .8378134
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. 
. /// Behavioral
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat hyper if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_hyper = r(StatTotal)
{txt}  5{com}. 
. tabstat hyper if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_hyper = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}{ralign 12:Variable} {...}
{c |}         N
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res}      113
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
   hyper
N {res}   113
{reset}
{ralign 12:Variable} {...}
{c |}         N
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res}       71
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
   hyper
N {res}    71
{reset}
{ralign 12:Variable} {...}
{c |}      Mean
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res} .2654867
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
          hyper
Mean {res} .26548673
{reset}
{ralign 12:Variable} {...}
{c |}      Mean
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res} .0704225
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
          hyper
Mean {res} .07042254
{reset}
{ralign 12:Variable} {...}
{c |}        SD
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res}  .443559
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
        hyper
SD {res} .44355905
{reset}
{ralign 12:Variable} {...}
{c |}        SD
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res} .2576789
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
        hyper
SD {res} .25767885
{reset}
{com}. 
. matrix n_hyper = J(1,1,.)
{txt}
{com}. forvalues i = 1/1 {c -(}
{txt}  2{com}.         matrix n_hyper[1,`i'] = n_tr_hyper[1,`i'] + n_ct_hyper[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in hyper{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment if hypernoinfo == 0, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:19})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}184
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}5.6
{col 69}{txt}max{col 72} = {res} 12
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                   hyper{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1950642{col 38}{space 1}   3.37{col 46}{space 3}0.008{col 54}{space 3} .0779486{col 66}{space 3} .3244927
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. 
. // significant level
. 
. local outcome DT_score_pre_std rosen_pre_std cpcs_pre_std hhmember hhheadage hhheadeduyear q2a q2c q2h tutor study_other followup_cog_std RSES_std CPCS_std
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}.                 if r2_`dep'_pv[1,1]<=0.01 {c -(}
{txt}  3{com}.                         local star_`dep' %3s "***"
{txt}  4{com}.                 {c )-}
{txt}  5{com}.                 else if (r2_`dep'_pv[1,1]>0.01) & (r2_`dep'_pv[1,1]<=0.05) {c -(}
{txt}  6{com}.                         local star_`dep' %2s "**"
{txt}  7{com}.                 {c )-}
{txt}  8{com}.                 else if (r2_`dep'_pv[1,1]>0.05) & (r2_`dep'_pv[1,1]<=0.10) {c -(}
{txt}  9{com}.                         local star_`dep' %1s "*"
{txt} 10{com}.                 {c )-}
{txt} 11{com}.                 else {c -(}
{txt} 12{com}.                         local star_`dep'  ""
{txt} 13{com}.                 {c )-}
{txt} 14{com}. {c )-} 
{txt}
{com}. 
. rwolf DT_score_pre_std rosen_pre_std cpcs_pre_std hhmember hhheadage hhheadeduyear, indepvar(treatment) reps(1000)
Bootstrap replications (1000). This may take some time.
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Romano-Wolf step-down adjusted p-values


Independent variable:  treatment
Outcome variables:   DT_score_pre_std rosen_pre_std cpcs_pre_std hhmember
{col 22}hhheadage hhheadeduyear
Number of resamples: 1000


{hline 78}
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
{hline 20}+{hline 57}
   {txt}DT_score_pre_std {c |}    {res}0.5518             0.5455              0.8541
      {txt}rosen_pre_std {c |}    {res}0.4689             0.4935              0.8541
       {txt}cpcs_pre_std {c |}    {res}0.0105             0.0100              0.0480
           {txt}hhmember {c |}    {res}0.1345             0.1169              0.4466
          {txt}hhheadage {c |}    {res}0.9229             0.9291              0.9291
      {txt}hhheadeduyear {c |}    {res}0.0494             0.0470              0.2148
{hline 78}
{txt}
{com}. rwolf q2a q2c q2h tutor study_other followup_cog_std RSES_std CPCS_std, indepvar(treatment) reps(1000)
Bootstrap replications (1000). This may take some time.
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Romano-Wolf step-down adjusted p-values


Independent variable:  treatment
Outcome variables:   q2a q2c q2h tutor study_other followup_cog_std RSES_std CPCS_std
Number of resamples: 1000


{hline 78}
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
{hline 20}+{hline 57}
                {txt}q2a {c |}    {res}0.7471             0.7522              0.7952
                {txt}q2c {c |}    {res}0.4722             0.4456              0.7952
                {txt}q2h {c |}    {res}0.2801             0.2697              0.5794
              {txt}tutor {c |}    {res}0.0573             0.0709              0.2258
        {txt}study_other {c |}    {res}0.6083             0.6094              0.7952
   {txt}followup_cog_std {c |}    {res}0.0809             0.0579              0.2418
           {txt}RSES_std {c |}    {res}0.0030             0.0030              0.0230
           {txt}CPCS_std {c |}    {res}0.0011             0.0010              0.0050
{hline 78}
{txt}
{com}. 
. 
. /// Table
> tempname hh2
{txt}
{com}. file open `hh2' using "$path_output/summary_stat.tex", write replace
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "% Author: Kazuma Takakura" _newline
{txt}
{com}. file write `hh2' "% Date: `c(current_date)'" _newline
{txt}
{com}. file write `hh2' "% Time: `c(current_time)'" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. 
. file write `hh2' "\begin{c -(}table{c )-}[h!]\footnotesize" _newline
{txt}
{com}. file write `hh2' "  \centering" _newline
{txt}
{com}. file write `hh2' "  \caption{c -(}Summary Statistics{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:sumstat{c )-}" _newline
{txt}
{com}. file write `hh2' "\scalebox{c -(}1{c )-}{c -(}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}threeparttable{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "\begin{c -(}tabular{c )-}{c -(}lccccc{c )-}\toprule" _newline
{txt}
{com}. 
.   
. file write `hh2' " Dependent Variable & Treatment &  Control  & Difference & N   \\\midrule\midrule" _newline
{txt}
{com}. file write `hh2' " Panel A: Baseline & & & &   \\ " _newline
{txt}
{com}. file write `hh2' " DT score^{c -(}a{c )-} & " %04.3f (mean_tr_bl[1,1]) " & " %04.3f (mean_ct_bl[1,1]) " & " %04.3f (r2_DT_score_pre_std_mean[1,1]) `star_DT_score_pre_std' " & " (n_bl[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_bl[1,1]) " ] & [ " %04.3f (sd_ct_bl[1,1]) " ] & ( " %04.3f (r2_DT_score_pre_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.831) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. 
. file write `hh2' " RSES score^{c -(}a{c )-} & " %04.3f (mean_tr_bl[1,2]) " & " %04.3f (mean_ct_bl[1,2]) " & " %04.3f (r2_rosen_pre_std_mean[1,1]) `star_rosen_pre_std' " & "  (n_bl[1,2]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_bl[1,2]) " ] & [ " %04.3f (sd_ct_bl[1,2]) " ] & ( " %04.3f (r2_rosen_pre_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.831) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " CPCS score^{c -(}a{c )-} & " %04.3f (mean_tr_bl[1,3]) " & " %04.3f (mean_ct_bl[1,3]) " & " %04.3f (r2_cpcs_pre_std_mean[1,1]) `star_cpcs_pre_std' " & "  (n_bl[1,3]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_bl[1,3]) " ] & [ " %04.3f (sd_ct_bl[1,3]) " ] & ( " %04.3f (r2_cpcs_pre_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.059) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Household size & " %04.3f (mean_tr_parent[1,1]) " & " %04.3f (mean_ct_parent[1,1]) " & " %04.3f (r2_hhmember_mean[1,1]) `star_hhmember'  " & " (n_parent[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_parent[1,1]) " ] & [ " %04.3f (sd_ct_parent[1,1]) " ] & ( " %04.3f (r2_hhmember_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.464) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Household head age & " %04.3f (mean_tr_parent[1,2]) " & " %04.3f (mean_ct_parent[1,2]) " & " %04.3f (r2_hhheadage_mean[1,1]) `star_hhheadage' " & "  (n_parent[1,2]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_parent[1,2]) " ] & [ " %04.3f (sd_ct_parent[1,2]) " ] & ( " %04.3f (r2_hhheadage_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.920) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Household head education & " %04.3f (mean_tr_parent[1,3]) " & " %04.3f (mean_ct_parent[1,3]) " & " %04.3f (r2_hhheadeduyear_mean[1,1]) `star_hhheadeduyear' " & "  (n_parent[1,3]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_parent[1,3]) " ] & [ " %04.3f (sd_ct_parent[1,3]) " ] & ( " %04.3f (r2_hhheadeduyear_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.248) " \{c )-} &   \\ " _newline
{txt}
{com}. file write `hh2' " \\ "_newline
{txt}
{com}. 
. file write `hh2' " Panel B: Follow-up & & & &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " School attendance & " %04.3f (mean_tr_school[1,1]) " & " %04.3f (mean_ct_school[1,1]) " & " %04.3f (r2_q2a_mean[1,1]) `star_q2a' " & " (n_school[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_school[1,1]) " ] & [ " %04.3f (sd_ct_school[1,1]) " ] & ( " %04.3f (r2_q2a_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.787) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Grade repeat & " %04.3f (mean_tr_school[1,3]) " & " %04.3f (mean_ct_school[1,3]) " & " %04.3f (r2_q2c_mean[1,1]) `star_q2c' " & "  (n_school[1,3]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_school[1,3]) " ] & [ " %04.3f (sd_ct_school[1,3]) " ] & ( " %04.3f (r2_q2c_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.787) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Drop out & " %04.3f (mean_tr_school[1,4]) " & " %04.3f (mean_ct_school[1,4]) " & " %04.3f (r2_q2h_mean[1,1]) `star_q2h'  " & "  (n_school[1,4]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_school[1,4]) " ] & [ " %04.3f (sd_ct_school[1,4]) " ] & ( " %04.3f (r2_q2h_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.576) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Tutoring & " %04.3f (mean_tr_study[1,1]) " & " %04.3f (mean_ct_study[1,1]) " & " %04.3f (r2_tutor_mean[1,1]) `star_tutor'  " & " (n_study[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_study[1,1]) " ] & [ " %04.3f (sd_ct_study[1,1]) " ] & ( " %04.3f (r2_tutor_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.230) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Self-study & " %04.3f (mean_tr_study[1,2]) " & " %04.3f (mean_ct_study[1,2]) " & " %04.3f (r2_study_other_mean[1,1]) `star_study_other' " & "  (n_study[1,2]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_study[1,2]) " ] & [ " %04.3f (sd_ct_study[1,2]) " ] & ( " %04.3f (r2_study_other_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.787) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Rapid math test score^{c -(}a{c )-} & " %04.3f (mean_tr_cog[1,1]) " & " %04.3f (mean_ct_cog[1,1]) " & " %04.3f (r2_followup_cog_std_mean[1,1]) `star_followup_cog_std'  "  & "  (n_cog[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_cog[1,1]) " ] & [ " %04.3f (sd_ct_cog[1,1]) " ] & ( " %04.3f (r2_followup_cog_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.270) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " RSES score^{c -(}a{c )-} & " %04.3f (mean_tr_noncog[1,2]) " & " %04.3f (mean_ct_noncog[1,2]) " & " %04.3f (r2_RSES_std_mean[1,1])   `star_RSES_std' " & " (n_noncog[1,2]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_noncog[1,2]) " ] & [ " %04.3f (sd_ct_noncog[1,2]) " ] & ( " %04.3f (r2_RSES_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.011) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " CPCS score^{c -(}a{c )-} & " %04.3f (mean_tr_noncog[1,3]) " & " %04.3f (mean_ct_noncog[1,3]) " & " %04.3f (r2_CPCS_std_mean[1,1])   `star_CPCS_std' "&  " (n_noncog[1,3]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_noncog[1,3]) " ] & [ " %04.3f (sd_ct_noncog[1,3]) " ] & ( " %04.3f (r2_CPCS_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.006) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. 
. file write `hh2' "\midrule" _newline
{txt}
{com}. file write `hh2' "\end{c -(}tabular{c )-}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\item (a) Variables are standardized using the average and variance of the whole follow-up sample in the February 2022 survey. " _newline
{txt}
{com}. file write `hh2' "\item (b) Standard deviations are reported in square brackets.  " _newline
{txt}
{com}. file write `hh2' "\item (c) Wild clustered bootstrap p-values are reported within parentheses. Clusters are schools at the baseline. There are 34 clusters. " _newline
{txt}
{com}. file write `hh2' "\item (d) Romano-Wolf multiple hypothesis testing p-values are reported in curly brackets. This test is conducted separately for the baseline variables and the follow-up variables." _newline
{txt}
{com}. file write `hh2' "\item (e) Statistical significance is indicated by stars based on the wild clustered bootstrap p-values reported in parentheses: $*$ denotes significance at the 10\% level, $∗∗$ at the 5\% level, and $∗∗∗$ at the 1\% level.  " _newline
{txt}
{com}. file write `hh2' "\end{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}threeparttable{c )-}" _newline
{txt}
{com}. file write `hh2' "{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}table{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. file close `hh2'
{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/mb/s9qljd594gbfyhl2dqnnwlcw0000gn/T//SD56186.000000"
{txt}
{com}. * This is the do file to create "Table 5. Heterogeneity by Baseline Abilites (Math and CPCS)"
. set seed 111
{txt}
{com}. 
. use "$path_data/temp/followup_student_baseline_score_missing_dummy", replace
{txt}
{com}. 
. egen DT_score_pre_std_med = median(DT_score_pre_std)
{txt}
{com}. gen DT_score_pre_std_upper50 = 1 if DT_score_pre_std>DT_score_pre_std_med
{txt}(121 missing values generated)

{com}. recode DT_score_pre_std_upper50 (.=0)
{txt}(121 changes made to {bf:DT_score_pre_std_upper50})

{com}. 
. egen rosen_pre_std_med = median(rosen_pre_std)
{txt}
{com}. gen rosen_pre_std_upper50 = 1 if rosen_pre_std>rosen_pre_std_med
{txt}(128 missing values generated)

{com}. recode rosen_pre_std_upper50 (.=0)
{txt}(128 changes made to {bf:rosen_pre_std_upper50})

{com}. rename rosen_pre_std_upper50 RSES_std_upper50
{res}{txt}
{com}. 
. egen cpcs_pre_std_med = median(cpcs_pre_std)
{txt}
{com}. gen cpcs_pre_std_upper50 = 1 if cpcs_pre_std>cpcs_pre_std_med
{txt}(135 missing values generated)

{com}. recode cpcs_pre_std_upper50 (.=0)
{txt}(135 changes made to {bf:cpcs_pre_std_upper50})

{com}. rename cpcs_pre_std_upper50 CPCS_std_upper50
{res}{txt}
{com}. 
. 
. /// Cognitive
> wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 59
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 22
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2397567{col 38}{space 1}  -0.86{col 46}{space 3}0.418{col 54}{space 3}-.7966316{col 66}{space 3}  .434311
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_rses_u = e(N)
{txt}
{com}. scalar n_clust_cog_u_rses_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.3459862{col 38}{space 1}  -1.14{col 46}{space 3}0.274{col 54}{space 3}-1.046262{col 66}{space 3}   .30228
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_rses_l = e(N)
{txt}
{com}. scalar n_clust_cog_u_rses_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2419353{col 38}{space 1}  -0.87{col 46}{space 3}0.364{col 54}{space 3}-.8619956{col 66}{space 3} .3835373
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_rses_u = e(N)
{txt}
{com}. scalar n_clust_cog_l_rses_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0781293{col 38}{space 1}  -0.29{col 46}{space 3}0.764{col 54}{space 3}-.6361322{col 66}{space 3} .4379878
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_rses_l = e(N)
{txt}
{com}. scalar n_clust_cog_l_rses_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:29})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 55
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0533319{col 38}{space 1}   0.17{col 46}{space 3}0.826{col 54}{space 3}-.6512296{col 66}{space 3} .8167328
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_cpcs_u = e(N)
{txt}
{com}. scalar n_clust_cog_u_cpcs_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 67
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.5660393{col 38}{space 1}  -1.84{col 46}{space 3}0.098{col 54}{space 3}-1.240136{col 66}{space 3}  .119599
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_cpcs_l = e(N)
{txt}
{com}. scalar n_clust_cog_u_cpcs_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0295186{col 38}{space 1}   0.10{col 46}{space 3}0.898{col 54}{space 3}-.5958441{col 66}{space 3} .6626134
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_cpcs_u = e(N)
{txt}
{com}. scalar n_clust_cog_l_cpcs_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:19})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 68
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  -.19243{col 38}{space 1}  -0.73{col 46}{space 3}0.482{col 54}{space 3}-.7246009{col 66}{space 3} .3950123
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_cpcs_l = e(N)
{txt}
{com}. scalar n_clust_cog_l_cpcs_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. 
. 
. /// RSES
> wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .9579135{col 38}{space 1}   3.36{col 46}{space 3}0.016{col 54}{space 3}   .26476{col 66}{space 3} 1.654666
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_u_cog_u = e(N)
{txt}
{com}. scalar n_clust_rses_u_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 61
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0615952{col 38}{space 1}   0.32{col 46}{space 3}0.746{col 54}{space 3}-.3207408{col 66}{space 3} .4426532
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_l_cog_u = e(N)
{txt}
{com}. scalar n_clust_rses_l_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .4920182{col 38}{space 1}   1.44{col 46}{space 3}0.206{col 54}{space 3}-.3791899{col 66}{space 3} 1.193207
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_u_cog_l = e(N)
{txt}
{com}. scalar n_clust_rses_u_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1388312{col 38}{space 1}   0.42{col 46}{space 3}0.678{col 54}{space 3}-.6031055{col 66}{space 3}  .913588
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_l_cog_l = e(N)
{txt}
{com}. scalar n_clust_rses_l_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:19})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 52
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 20
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .6664341{col 38}{space 1}   3.26{col 46}{space 3}0.012{col 54}{space 3} .2321136{col 66}{space 3} 1.106288
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3847293{col 38}{space 1}   1.53{col 46}{space 3}0.124{col 54}{space 3}-.1218257{col 66}{space 3} .9801306
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2062219{col 38}{space 1}   0.47{col 46}{space 3}0.686{col 54}{space 3}-1.027415{col 66}{space 3} 1.180279
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 66
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3332931{col 38}{space 1}   0.96{col 46}{space 3}0.380{col 54}{space 3}-.4877823{col 66}{space 3} 1.105785
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. 
. 
. /// CPCS
> 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} 1.020132{col 38}{space 1}   3.66{col 46}{space 3}0.006{col 54}{space 3} .3600309{col 66}{space 3} 1.627818
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 61
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0665639{col 38}{space 1}   0.34{col 46}{space 3}0.810{col 54}{space 3}-.3876734{col 66}{space 3} .4535088
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .5458399{col 38}{space 1}   1.63{col 46}{space 3}0.144{col 54}{space 3}-.2669703{col 66}{space 3} 1.253775
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1431476{col 38}{space 1}   0.41{col 46}{space 3}0.704{col 54}{space 3}-.6225786{col 66}{space 3} 1.047643
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 52
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 20
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  .774167{col 38}{space 1}   3.68{col 46}{space 3}0.002{col 54}{space 3} .3283526{col 66}{space 3} 1.235857
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_u_cog_u = e(N)
{txt}
{com}. scalar n_clust_cpcs_u_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3575321{col 38}{space 1}   1.51{col 46}{space 3}0.162{col 54}{space 3}-.1390679{col 66}{space 3} .8784332
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_l_cog_u = e(N)
{txt}
{com}. scalar n_clust_cpcs_l_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2792052{col 38}{space 1}   0.63{col 46}{space 3}0.554{col 54}{space 3}-.8321789{col 66}{space 3} 1.362015
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_u_cog_l = e(N)
{txt}
{com}. scalar n_clust_cpcs_u_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 66
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}   .33148{col 38}{space 1}   0.95{col 46}{space 3}0.352{col 54}{space 3}-.4706789{col 66}{space 3} 1.158291
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_l_cog_l = e(N)
{txt}
{com}. scalar n_clust_cpcs_l_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. // significant level
. 
. 
. local outcome cog rses cpcs
{txt}
{com}. local hetero1 cogU cogL
{txt}
{com}. local hetero2 rsesU rsesL
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. foreach h1 in `hetero1'{c -(}
{txt}  3{com}. foreach h2 in `hetero2'{c -(}
{txt}  4{com}.                 if `dep'_`h1'_`h2'_pv[1,1]<=0.01 {c -(}
{txt}  5{com}.                         local star_`dep'_`h1'_`h2' %3s "***"
{txt}  6{com}.                 {c )-}
{txt}  7{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.01) & (`dep'_`h1'_`h2'_pv[1,1]<=0.05) {c -(}
{txt}  8{com}.                         local star_`dep'_`h1'_`h2' %2s "**"
{txt}  9{com}.                 {c )-}
{txt} 10{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.05) & (`dep'_`h1'_`h2'_pv[1,1]<=0.10) {c -(}
{txt} 11{com}.                         local star_`dep'_`h1'_`h2' %1s "*"
{txt} 12{com}.                 {c )-}
{txt} 13{com}.                 else {c -(}
{txt} 14{com}.                         local star_`dep'_`h1'_`h2'  ""
{txt} 15{com}.                 {c )-}
{txt} 16{com}. {c )-} 
{txt} 17{com}. {c )-}
{txt} 18{com}. {c )-}
{txt}
{com}. 
. local outcome cog rses cpcs
{txt}
{com}. local hetero1 cogU cogL
{txt}
{com}. local hetero2 cpcsU cpcsL
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. foreach h1 in `hetero1'{c -(}
{txt}  3{com}. foreach h2 in `hetero2'{c -(}
{txt}  4{com}.                 if `dep'_`h1'_`h2'_pv[1,1]<=0.01 {c -(}
{txt}  5{com}.                         local star_`dep'_`h1'_`h2' %3s "***"
{txt}  6{com}.                 {c )-}
{txt}  7{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.01) & (`dep'_`h1'_`h2'_pv[1,1]<=0.05) {c -(}
{txt}  8{com}.                         local star_`dep'_`h1'_`h2' %2s "**"
{txt}  9{com}.                 {c )-}
{txt} 10{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.05) & (`dep'_`h1'_`h2'_pv[1,1]<=0.10) {c -(}
{txt} 11{com}.                         local star_`dep'_`h1'_`h2' %1s "*"
{txt} 12{com}.                 {c )-}
{txt} 13{com}.                 else {c -(}
{txt} 14{com}.                         local star_`dep'_`h1'_`h2'  ""
{txt} 15{com}.                 {c )-}
{txt} 16{com}. {c )-} 
{txt} 17{com}. {c )-}
{txt} 18{com}. {c )-}
{txt}
{com}. 
. /// Table
> 
. tempname hh2
{txt}
{com}. file open `hh2' using "$path_output/hetero_2by2_CPCS.tex", write replace
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "% Author: Kazuma Takakura" _newline
{txt}
{com}. file write `hh2' "% Date: `c(current_date)'" _newline
{txt}
{com}. file write `hh2' "% Time: `c(current_time)'" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. 
. file write `hh2' "\begin{c -(}table{c )-}[h!]\footnotesize" _newline
{txt}
{com}. file write `hh2' "  \centering" _newline
{txt}
{com}. file write `hh2' "  \caption{c -(}Heterogeneity among Baseline Abilites (Math and CPCS){c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:hetero_cpcs{c )-}" _newline
{txt}
{com}. file write `hh2' "\scalebox{c -(}1{c )-}{c -(}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}threeparttable{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "\begin{c -(}tabular{c )-}{c -(}lccccccc{c )-}\toprule" _newline
{txt}
{com}. 
. file write `hh2' " Dependent Variable & \multicolumn{c -(}2{c )-}{c -(}c{c )-}{c -(}Baseline^{c -(}b{c )-}{c )-} & Difference & Obs & N of clusters  \\\midrule\midrule" _newline
{txt}
{com}. file write `hh2' " Rapid math test score^{c -(}a{c )-} & Math Top 50\%  & CPCS Top 50\% & " %04.3f (cog_cogU_rsesU_mean[1,1]) `star_cog_cogU_cpcsU' " & " %02.0f ( n_cog_u_cpcs_u ) " & " %02.0f ( n_clust_cog_u_cpcs_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogU_cpcsU_pv[1,1]) " )  & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cog_cogU_cpcsL_mean[1,1]) `star_cog_cogU_cpcsL' " & " %02.0f ( n_cog_u_cpcs_l ) " & " %02.0f ( n_clust_cog_u_cpcs_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogU_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & Math Bottom 50\%  & CPCS Top 50\% & " %04.3f (cog_cogL_cpcsU_mean[1,1]) `star_cog_cogL_cpcsU' " & " %02.0f ( n_cog_l_cpcs_u ) " & " %02.0f ( n_clust_cog_l_cpcs_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogL_cpcsU_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cog_cogL_cpcsL_mean[1,1]) `star_cog_cogL_cpcsL' " & " %02.0f ( n_cog_l_cpcs_l ) " & " %02.0f ( n_clust_cog_l_cpcs_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogL_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. 
. file write `hh2' " CPCS score^{c -(}a{c )-} & Math Top 50\%  & CPCS Top 50\% & " %04.3f (cpcs_cogU_cpcsU_mean[1,1]) `star_cpcs_cogU_cpcsU' " & " %02.0f ( n_cpcs_u_cog_u ) " & " %02.0f ( n_clust_cpcs_u_cog_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cpcs_cogU_cpcsU_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cpcs_cogU_cpcsL_mean[1,1]) `star_cpcs_cogU_cpcsL' " & " %02.0f ( n_cpcs_l_cog_u ) " & " %02.0f ( n_clust_cpcs_l_cog_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &       & & ( " %04.3f (cpcs_cogU_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & Math Bottom 50\%  & CPCS Top 50\% & " %04.3f (cpcs_cogL_cpcsU_mean[1,1]) `star_cpcs_cogL_cpcsU' " & " %02.0f ( n_cpcs_u_cog_l ) " & " %02.0f ( n_clust_cpcs_u_cog_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cpcs_cogL_cpcsU_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cpcs_cogL_cpcsL_mean[1,1]) `star_cpcs_cogL_cpcsL' " & " %02.0f ( n_cpcs_l_cog_l ) " & " %02.0f ( n_clust_cpcs_l_cog_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &       & & ( " %04.3f (cpcs_cogL_cpcsL_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. 
. file write `hh2' "\\\midrule" _newline
{txt}
{com}. file write `hh2' "\end{c -(}tabular{c )-}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\item (a) Dependent variables are standardized using the average and variance of the whole follow-up sample in the February 2022 survey. " _newline
{txt}
{com}. file write `hh2' "\item (b) Cutoffs are created based on whether their ability to perform each item at the baseline was higher or lower than the median. " _newline
{txt}
{com}. file write `hh2' "\item (c) Wild cluster bootstrap p-values are reported within parentheses. Clusters are schools at the baseline." _newline
{txt}
{com}. file write `hh2' "\item (d) $^*$ Significant at 10\% level; $^{c -(}**{c )-}$ significant at 5\% level; $^{c -(}***{c )-}$ significant at 1\% level. " _newline
{txt}
{com}. file write `hh2' "\end{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}threeparttable{c )-}" _newline
{txt}
{com}. file write `hh2' "{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:addlabel{c )-}%" _newline
{txt}
{com}. file write `hh2' "\end{c -(}table{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. file close `hh2'
{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/mb/s9qljd594gbfyhl2dqnnwlcw0000gn/T//SD56186.000000"
{txt}
{com}. * This is the do file to create "Table 5. Heterogeneity by Baseline Abilites (Math and CPCS)"
. set seed 123456
{txt}
{com}. 
. use "$path_data/temp/followup_student_baseline_score_missing_dummy", replace
{txt}
{com}. 
. egen DT_score_pre_std_med = median(DT_score_pre_std)
{txt}
{com}. gen DT_score_pre_std_upper50 = 1 if DT_score_pre_std>DT_score_pre_std_med
{txt}(121 missing values generated)

{com}. recode DT_score_pre_std_upper50 (.=0)
{txt}(121 changes made to {bf:DT_score_pre_std_upper50})

{com}. 
. egen rosen_pre_std_med = median(rosen_pre_std)
{txt}
{com}. gen rosen_pre_std_upper50 = 1 if rosen_pre_std>rosen_pre_std_med
{txt}(128 missing values generated)

{com}. recode rosen_pre_std_upper50 (.=0)
{txt}(128 changes made to {bf:rosen_pre_std_upper50})

{com}. rename rosen_pre_std_upper50 RSES_std_upper50
{res}{txt}
{com}. 
. egen cpcs_pre_std_med = median(cpcs_pre_std)
{txt}
{com}. gen cpcs_pre_std_upper50 = 1 if cpcs_pre_std>cpcs_pre_std_med
{txt}(135 missing values generated)

{com}. recode cpcs_pre_std_upper50 (.=0)
{txt}(135 changes made to {bf:cpcs_pre_std_upper50})

{com}. rename cpcs_pre_std_upper50 CPCS_std_upper50
{res}{txt}
{com}. 
. 
. /// Cognitive
> wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 59
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 22
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2397567{col 38}{space 1}  -0.86{col 46}{space 3}0.430{col 54}{space 3}-.8032403{col 66}{space 3} .3716752
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_rses_u = e(N)
{txt}
{com}. scalar n_clust_cog_u_rses_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:19})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.3459862{col 38}{space 1}  -1.14{col 46}{space 3}0.244{col 54}{space 3}-1.015253{col 66}{space 3} .2830204
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_rses_l = e(N)
{txt}
{com}. scalar n_clust_cog_u_rses_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2419353{col 38}{space 1}  -0.87{col 46}{space 3}0.410{col 54}{space 3}-.8329826{col 66}{space 3}  .438376
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_rses_u = e(N)
{txt}
{com}. scalar n_clust_cog_l_rses_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0781293{col 38}{space 1}  -0.29{col 46}{space 3}0.770{col 54}{space 3}-.5848532{col 66}{space 3}  .496441
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_rses_l = e(N)
{txt}
{com}. scalar n_clust_cog_l_rses_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 55
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0533319{col 38}{space 1}   0.17{col 46}{space 3}0.808{col 54}{space 3}-.6044358{col 66}{space 3} .7809246
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_cpcs_u = e(N)
{txt}
{com}. scalar n_clust_cog_u_cpcs_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 67
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.5660393{col 38}{space 1}  -1.84{col 46}{space 3}0.092{col 54}{space 3}-1.190451{col 66}{space 3} .1242038
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_cpcs_l = e(N)
{txt}
{com}. scalar n_clust_cog_u_cpcs_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0295186{col 38}{space 1}   0.10{col 46}{space 3}0.962{col 54}{space 3}-.5820749{col 66}{space 3} .7418611
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_cpcs_u = e(N)
{txt}
{com}. scalar n_clust_cog_l_cpcs_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 68
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  -.19243{col 38}{space 1}  -0.73{col 46}{space 3}0.484{col 54}{space 3}-.7372303{col 66}{space 3} .3723262
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_cpcs_l = e(N)
{txt}
{com}. scalar n_clust_cog_l_cpcs_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. 
. 
. /// RSES
> wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .9579135{col 38}{space 1}   3.36{col 46}{space 3}0.012{col 54}{space 3} .3102337{col 66}{space 3}  1.66492
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_u_cog_u = e(N)
{txt}
{com}. scalar n_clust_rses_u_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 61
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0615952{col 38}{space 1}   0.32{col 46}{space 3}0.750{col 54}{space 3}-.3644986{col 66}{space 3} .4502712
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_l_cog_u = e(N)
{txt}
{com}. scalar n_clust_rses_l_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .4920182{col 38}{space 1}   1.44{col 46}{space 3}0.188{col 54}{space 3}-.3104861{col 66}{space 3} 1.221096
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_u_cog_l = e(N)
{txt}
{com}. scalar n_clust_rses_u_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1388312{col 38}{space 1}   0.42{col 46}{space 3}0.682{col 54}{space 3}-.6196358{col 66}{space 3} .9070666
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_l_cog_l = e(N)
{txt}
{com}. scalar n_clust_rses_l_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 52
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 20
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .6664341{col 38}{space 1}   3.26{col 46}{space 3}0.008{col 54}{space 3} .2503958{col 66}{space 3} 1.149229
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3847293{col 38}{space 1}   1.53{col 46}{space 3}0.116{col 54}{space 3}-.0983357{col 66}{space 3}  .928789
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2062219{col 38}{space 1}   0.47{col 46}{space 3}0.712{col 54}{space 3}-.9964304{col 66}{space 3} 1.115433
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 66
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3332931{col 38}{space 1}   0.96{col 46}{space 3}0.424{col 54}{space 3}-.5309029{col 66}{space 3} 1.116114
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. 
. 
. /// CPCS
> 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} 1.020132{col 38}{space 1}   3.66{col 46}{space 3}0.002{col 54}{space 3} .3957498{col 66}{space 3} 1.677183
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 61
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0665639{col 38}{space 1}   0.34{col 46}{space 3}0.702{col 54}{space 3}-.3660282{col 66}{space 3}  .472553
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .5458399{col 38}{space 1}   1.63{col 46}{space 3}0.170{col 54}{space 3}-.2853186{col 66}{space 3} 1.213484
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1431476{col 38}{space 1}   0.41{col 46}{space 3}0.744{col 54}{space 3}-.6217227{col 66}{space 3} .9566137
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 52
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 20
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  .774167{col 38}{space 1}   3.68{col 46}{space 3}0.002{col 54}{space 3} .2954373{col 66}{space 3} 1.292805
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_u_cog_u = e(N)
{txt}
{com}. scalar n_clust_cpcs_u_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3575321{col 38}{space 1}   1.51{col 46}{space 3}0.134{col 54}{space 3}-.0968285{col 66}{space 3}  .850778
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_l_cog_u = e(N)
{txt}
{com}. scalar n_clust_cpcs_l_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2792052{col 38}{space 1}   0.63{col 46}{space 3}0.560{col 54}{space 3} -.891793{col 66}{space 3} 1.321435
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_u_cog_l = e(N)
{txt}
{com}. scalar n_clust_cpcs_u_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 66
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}   .33148{col 38}{space 1}   0.95{col 46}{space 3}0.422{col 54}{space 3}-.5339144{col 66}{space 3} 1.054402
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_l_cog_l = e(N)
{txt}
{com}. scalar n_clust_cpcs_l_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. // significant level
. 
. 
. local outcome cog rses cpcs
{txt}
{com}. local hetero1 cogU cogL
{txt}
{com}. local hetero2 rsesU rsesL
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. foreach h1 in `hetero1'{c -(}
{txt}  3{com}. foreach h2 in `hetero2'{c -(}
{txt}  4{com}.                 if `dep'_`h1'_`h2'_pv[1,1]<=0.01 {c -(}
{txt}  5{com}.                         local star_`dep'_`h1'_`h2' %3s "***"
{txt}  6{com}.                 {c )-}
{txt}  7{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.01) & (`dep'_`h1'_`h2'_pv[1,1]<=0.05) {c -(}
{txt}  8{com}.                         local star_`dep'_`h1'_`h2' %2s "**"
{txt}  9{com}.                 {c )-}
{txt} 10{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.05) & (`dep'_`h1'_`h2'_pv[1,1]<=0.10) {c -(}
{txt} 11{com}.                         local star_`dep'_`h1'_`h2' %1s "*"
{txt} 12{com}.                 {c )-}
{txt} 13{com}.                 else {c -(}
{txt} 14{com}.                         local star_`dep'_`h1'_`h2'  ""
{txt} 15{com}.                 {c )-}
{txt} 16{com}. {c )-} 
{txt} 17{com}. {c )-}
{txt} 18{com}. {c )-}
{txt}
{com}. 
. local outcome cog rses cpcs
{txt}
{com}. local hetero1 cogU cogL
{txt}
{com}. local hetero2 cpcsU cpcsL
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. foreach h1 in `hetero1'{c -(}
{txt}  3{com}. foreach h2 in `hetero2'{c -(}
{txt}  4{com}.                 if `dep'_`h1'_`h2'_pv[1,1]<=0.01 {c -(}
{txt}  5{com}.                         local star_`dep'_`h1'_`h2' %3s "***"
{txt}  6{com}.                 {c )-}
{txt}  7{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.01) & (`dep'_`h1'_`h2'_pv[1,1]<=0.05) {c -(}
{txt}  8{com}.                         local star_`dep'_`h1'_`h2' %2s "**"
{txt}  9{com}.                 {c )-}
{txt} 10{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.05) & (`dep'_`h1'_`h2'_pv[1,1]<=0.10) {c -(}
{txt} 11{com}.                         local star_`dep'_`h1'_`h2' %1s "*"
{txt} 12{com}.                 {c )-}
{txt} 13{com}.                 else {c -(}
{txt} 14{com}.                         local star_`dep'_`h1'_`h2'  ""
{txt} 15{com}.                 {c )-}
{txt} 16{com}. {c )-} 
{txt} 17{com}. {c )-}
{txt} 18{com}. {c )-}
{txt}
{com}. 
. /// Table
> 
. tempname hh2
{txt}
{com}. file open `hh2' using "$path_output/hetero_2by2_CPCS.tex", write replace
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "% Author: Kazuma Takakura" _newline
{txt}
{com}. file write `hh2' "% Date: `c(current_date)'" _newline
{txt}
{com}. file write `hh2' "% Time: `c(current_time)'" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. 
. file write `hh2' "\begin{c -(}table{c )-}[h!]\footnotesize" _newline
{txt}
{com}. file write `hh2' "  \centering" _newline
{txt}
{com}. file write `hh2' "  \caption{c -(}Heterogeneity among Baseline Abilites (Math and CPCS){c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:hetero_cpcs{c )-}" _newline
{txt}
{com}. file write `hh2' "\scalebox{c -(}1{c )-}{c -(}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}threeparttable{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "\begin{c -(}tabular{c )-}{c -(}lccccccc{c )-}\toprule" _newline
{txt}
{com}. 
. file write `hh2' " Dependent Variable & \multicolumn{c -(}2{c )-}{c -(}c{c )-}{c -(}Baseline^{c -(}b{c )-}{c )-} & Difference & Obs & N of clusters  \\\midrule\midrule" _newline
{txt}
{com}. file write `hh2' " Rapid math test score^{c -(}a{c )-} & Math Top 50\%  & CPCS Top 50\% & " %04.3f (cog_cogU_rsesU_mean[1,1]) `star_cog_cogU_cpcsU' " & " %02.0f ( n_cog_u_cpcs_u ) " & " %02.0f ( n_clust_cog_u_cpcs_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogU_cpcsU_pv[1,1]) " )  & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cog_cogU_cpcsL_mean[1,1]) `star_cog_cogU_cpcsL' " & " %02.0f ( n_cog_u_cpcs_l ) " & " %02.0f ( n_clust_cog_u_cpcs_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogU_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & Math Bottom 50\%  & CPCS Top 50\% & " %04.3f (cog_cogL_cpcsU_mean[1,1]) `star_cog_cogL_cpcsU' " & " %02.0f ( n_cog_l_cpcs_u ) " & " %02.0f ( n_clust_cog_l_cpcs_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogL_cpcsU_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cog_cogL_cpcsL_mean[1,1]) `star_cog_cogL_cpcsL' " & " %02.0f ( n_cog_l_cpcs_l ) " & " %02.0f ( n_clust_cog_l_cpcs_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogL_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. 
. file write `hh2' " CPCS score^{c -(}a{c )-} & Math Top 50\%  & CPCS Top 50\% & " %04.3f (cpcs_cogU_cpcsU_mean[1,1]) `star_cpcs_cogU_cpcsU' " & " %02.0f ( n_cpcs_u_cog_u ) " & " %02.0f ( n_clust_cpcs_u_cog_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cpcs_cogU_cpcsU_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cpcs_cogU_cpcsL_mean[1,1]) `star_cpcs_cogU_cpcsL' " & " %02.0f ( n_cpcs_l_cog_u ) " & " %02.0f ( n_clust_cpcs_l_cog_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &       & & ( " %04.3f (cpcs_cogU_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & Math Bottom 50\%  & CPCS Top 50\% & " %04.3f (cpcs_cogL_cpcsU_mean[1,1]) `star_cpcs_cogL_cpcsU' " & " %02.0f ( n_cpcs_u_cog_l ) " & " %02.0f ( n_clust_cpcs_u_cog_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cpcs_cogL_cpcsU_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cpcs_cogL_cpcsL_mean[1,1]) `star_cpcs_cogL_cpcsL' " & " %02.0f ( n_cpcs_l_cog_l ) " & " %02.0f ( n_clust_cpcs_l_cog_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &       & & ( " %04.3f (cpcs_cogL_cpcsL_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. 
. file write `hh2' "\\\midrule" _newline
{txt}
{com}. file write `hh2' "\end{c -(}tabular{c )-}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\item (a) Dependent variables are standardized using the average and variance of the whole follow-up sample in the February 2022 survey. " _newline
{txt}
{com}. file write `hh2' "\item (b) Cutoffs are created based on whether their ability to perform each item at the baseline was higher or lower than the median. " _newline
{txt}
{com}. file write `hh2' "\item (c) Wild cluster bootstrap p-values are reported within parentheses. Clusters are schools at the baseline." _newline
{txt}
{com}. file write `hh2' "\item (d) $^*$ Significant at 10\% level; $^{c -(}**{c )-}$ significant at 5\% level; $^{c -(}***{c )-}$ significant at 1\% level. " _newline
{txt}
{com}. file write `hh2' "\end{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}threeparttable{c )-}" _newline
{txt}
{com}. file write `hh2' "{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:addlabel{c )-}%" _newline
{txt}
{com}. file write `hh2' "\end{c -(}table{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. file close `hh2'
{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/mb/s9qljd594gbfyhl2dqnnwlcw0000gn/T//SD56186.000000"
{txt}
{com}. * This is the do file to create "Table 5. Heterogeneity by Baseline Abilites (Math and CPCS)"
. set seed 1
{txt}
{com}. 
. use "$path_data/temp/followup_student_baseline_score_missing_dummy", replace
{txt}
{com}. 
. egen DT_score_pre_std_med = median(DT_score_pre_std)
{txt}
{com}. gen DT_score_pre_std_upper50 = 1 if DT_score_pre_std>DT_score_pre_std_med
{txt}(121 missing values generated)

{com}. recode DT_score_pre_std_upper50 (.=0)
{txt}(121 changes made to {bf:DT_score_pre_std_upper50})

{com}. 
. egen rosen_pre_std_med = median(rosen_pre_std)
{txt}
{com}. gen rosen_pre_std_upper50 = 1 if rosen_pre_std>rosen_pre_std_med
{txt}(128 missing values generated)

{com}. recode rosen_pre_std_upper50 (.=0)
{txt}(128 changes made to {bf:rosen_pre_std_upper50})

{com}. rename rosen_pre_std_upper50 RSES_std_upper50
{res}{txt}
{com}. 
. egen cpcs_pre_std_med = median(cpcs_pre_std)
{txt}
{com}. gen cpcs_pre_std_upper50 = 1 if cpcs_pre_std>cpcs_pre_std_med
{txt}(135 missing values generated)

{com}. recode cpcs_pre_std_upper50 (.=0)
{txt}(135 changes made to {bf:cpcs_pre_std_upper50})

{com}. rename cpcs_pre_std_upper50 CPCS_std_upper50
{res}{txt}
{com}. 
. 
. /// Cognitive
> wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 59
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 22
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2397567{col 38}{space 1}  -0.86{col 46}{space 3}0.412{col 54}{space 3}-.8323853{col 66}{space 3} .4175037
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_rses_u = e(N)
{txt}
{com}. scalar n_clust_cog_u_rses_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.3459862{col 38}{space 1}  -1.14{col 46}{space 3}0.252{col 54}{space 3}-.9957895{col 66}{space 3} .2894481
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_rses_l = e(N)
{txt}
{com}. scalar n_clust_cog_u_rses_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2419353{col 38}{space 1}  -0.87{col 46}{space 3}0.414{col 54}{space 3}-.7989934{col 66}{space 3} .4263604
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_rses_u = e(N)
{txt}
{com}. scalar n_clust_cog_l_rses_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0781293{col 38}{space 1}  -0.29{col 46}{space 3}0.798{col 54}{space 3}-.6024584{col 66}{space 3} .5774076
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_rses_l = e(N)
{txt}
{com}. scalar n_clust_cog_l_rses_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 55
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0533319{col 38}{space 1}   0.17{col 46}{space 3}0.876{col 54}{space 3}-.6050722{col 66}{space 3} .7471398
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_cpcs_u = e(N)
{txt}
{com}. scalar n_clust_cog_u_cpcs_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 67
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.5660393{col 38}{space 1}  -1.84{col 46}{space 3}0.058{col 54}{space 3}-1.200423{col 66}{space 3} .0265449
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_cpcs_l = e(N)
{txt}
{com}. scalar n_clust_cog_u_cpcs_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0295186{col 38}{space 1}   0.10{col 46}{space 3}0.890{col 54}{space 3}-.6279539{col 66}{space 3} .7132061
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_cpcs_u = e(N)
{txt}
{com}. scalar n_clust_cog_l_cpcs_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 68
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  -.19243{col 38}{space 1}  -0.73{col 46}{space 3}0.504{col 54}{space 3}-.7344232{col 66}{space 3} .3561501
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_cpcs_l = e(N)
{txt}
{com}. scalar n_clust_cog_l_cpcs_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. 
. 
. /// RSES
> wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:19})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .9579135{col 38}{space 1}   3.36{col 46}{space 3}0.012{col 54}{space 3} .2718992{col 66}{space 3} 1.597382
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_u_cog_u = e(N)
{txt}
{com}. scalar n_clust_rses_u_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 61
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0615952{col 38}{space 1}   0.32{col 46}{space 3}0.804{col 54}{space 3} -.346128{col 66}{space 3} .4345873
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_l_cog_u = e(N)
{txt}
{com}. scalar n_clust_rses_l_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .4920182{col 38}{space 1}   1.44{col 46}{space 3}0.182{col 54}{space 3}-.3275928{col 66}{space 3} 1.187104
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_u_cog_l = e(N)
{txt}
{com}. scalar n_clust_rses_u_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1388312{col 38}{space 1}   0.42{col 46}{space 3}0.674{col 54}{space 3}-.6025065{col 66}{space 3} .9137282
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_l_cog_l = e(N)
{txt}
{com}. scalar n_clust_rses_l_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 52
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 20
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .6664341{col 38}{space 1}   3.26{col 46}{space 3}0.010{col 54}{space 3}  .223697{col 66}{space 3} 1.128999
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3847293{col 38}{space 1}   1.53{col 46}{space 3}0.150{col 54}{space 3}-.1528505{col 66}{space 3} .9279308
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2062219{col 38}{space 1}   0.47{col 46}{space 3}0.642{col 54}{space 3}-.8851328{col 66}{space 3}  1.11006
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 66
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3332931{col 38}{space 1}   0.96{col 46}{space 3}0.388{col 54}{space 3}-.4439605{col 66}{space 3} 1.111003
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. 
. 
. /// CPCS
> 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:19})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} 1.020132{col 38}{space 1}   3.66{col 46}{space 3}0.006{col 54}{space 3} .3435691{col 66}{space 3} 1.713353
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 61
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0665639{col 38}{space 1}   0.34{col 46}{space 3}0.720{col 54}{space 3}-.3345648{col 66}{space 3} .4862635
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .5458399{col 38}{space 1}   1.63{col 46}{space 3}0.134{col 54}{space 3}-.2092327{col 66}{space 3} 1.233199
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1431476{col 38}{space 1}   0.41{col 46}{space 3}0.696{col 54}{space 3}-.6640145{col 66}{space 3} .9784031
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 52
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 20
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  .774167{col 38}{space 1}   3.68{col 46}{space 3}0.002{col 54}{space 3} .3112148{col 66}{space 3} 1.305168
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_u_cog_u = e(N)
{txt}
{com}. scalar n_clust_cpcs_u_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3575321{col 38}{space 1}   1.51{col 46}{space 3}0.144{col 54}{space 3}-.1375077{col 66}{space 3} .8888439
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_l_cog_u = e(N)
{txt}
{com}. scalar n_clust_cpcs_l_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2792052{col 38}{space 1}   0.63{col 46}{space 3}0.524{col 54}{space 3}-.8592776{col 66}{space 3} 1.292293
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_u_cog_l = e(N)
{txt}
{com}. scalar n_clust_cpcs_u_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 66
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}   .33148{col 38}{space 1}   0.95{col 46}{space 3}0.384{col 54}{space 3}-.4816208{col 66}{space 3} 1.038965
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_l_cog_l = e(N)
{txt}
{com}. scalar n_clust_cpcs_l_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. // significant level
. 
. 
. local outcome cog rses cpcs
{txt}
{com}. local hetero1 cogU cogL
{txt}
{com}. local hetero2 rsesU rsesL
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. foreach h1 in `hetero1'{c -(}
{txt}  3{com}. foreach h2 in `hetero2'{c -(}
{txt}  4{com}.                 if `dep'_`h1'_`h2'_pv[1,1]<=0.01 {c -(}
{txt}  5{com}.                         local star_`dep'_`h1'_`h2' %3s "***"
{txt}  6{com}.                 {c )-}
{txt}  7{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.01) & (`dep'_`h1'_`h2'_pv[1,1]<=0.05) {c -(}
{txt}  8{com}.                         local star_`dep'_`h1'_`h2' %2s "**"
{txt}  9{com}.                 {c )-}
{txt} 10{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.05) & (`dep'_`h1'_`h2'_pv[1,1]<=0.10) {c -(}
{txt} 11{com}.                         local star_`dep'_`h1'_`h2' %1s "*"
{txt} 12{com}.                 {c )-}
{txt} 13{com}.                 else {c -(}
{txt} 14{com}.                         local star_`dep'_`h1'_`h2'  ""
{txt} 15{com}.                 {c )-}
{txt} 16{com}. {c )-} 
{txt} 17{com}. {c )-}
{txt} 18{com}. {c )-}
{txt}
{com}. 
. local outcome cog rses cpcs
{txt}
{com}. local hetero1 cogU cogL
{txt}
{com}. local hetero2 cpcsU cpcsL
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. foreach h1 in `hetero1'{c -(}
{txt}  3{com}. foreach h2 in `hetero2'{c -(}
{txt}  4{com}.                 if `dep'_`h1'_`h2'_pv[1,1]<=0.01 {c -(}
{txt}  5{com}.                         local star_`dep'_`h1'_`h2' %3s "***"
{txt}  6{com}.                 {c )-}
{txt}  7{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.01) & (`dep'_`h1'_`h2'_pv[1,1]<=0.05) {c -(}
{txt}  8{com}.                         local star_`dep'_`h1'_`h2' %2s "**"
{txt}  9{com}.                 {c )-}
{txt} 10{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.05) & (`dep'_`h1'_`h2'_pv[1,1]<=0.10) {c -(}
{txt} 11{com}.                         local star_`dep'_`h1'_`h2' %1s "*"
{txt} 12{com}.                 {c )-}
{txt} 13{com}.                 else {c -(}
{txt} 14{com}.                         local star_`dep'_`h1'_`h2'  ""
{txt} 15{com}.                 {c )-}
{txt} 16{com}. {c )-} 
{txt} 17{com}. {c )-}
{txt} 18{com}. {c )-}
{txt}
{com}. 
. /// Table
> 
. tempname hh2
{txt}
{com}. file open `hh2' using "$path_output/hetero_2by2_CPCS.tex", write replace
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "% Author: Kazuma Takakura" _newline
{txt}
{com}. file write `hh2' "% Date: `c(current_date)'" _newline
{txt}
{com}. file write `hh2' "% Time: `c(current_time)'" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. 
. file write `hh2' "\begin{c -(}table{c )-}[h!]\footnotesize" _newline
{txt}
{com}. file write `hh2' "  \centering" _newline
{txt}
{com}. file write `hh2' "  \caption{c -(}Heterogeneity among Baseline Abilites (Math and CPCS){c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:hetero_cpcs{c )-}" _newline
{txt}
{com}. file write `hh2' "\scalebox{c -(}1{c )-}{c -(}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}threeparttable{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "\begin{c -(}tabular{c )-}{c -(}lccccccc{c )-}\toprule" _newline
{txt}
{com}. 
. file write `hh2' " Dependent Variable & \multicolumn{c -(}2{c )-}{c -(}c{c )-}{c -(}Baseline^{c -(}b{c )-}{c )-} & Difference & Obs & N of clusters  \\\midrule\midrule" _newline
{txt}
{com}. file write `hh2' " Rapid math test score^{c -(}a{c )-} & Math Top 50\%  & CPCS Top 50\% & " %04.3f (cog_cogU_rsesU_mean[1,1]) `star_cog_cogU_cpcsU' " & " %02.0f ( n_cog_u_cpcs_u ) " & " %02.0f ( n_clust_cog_u_cpcs_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogU_cpcsU_pv[1,1]) " )  & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cog_cogU_cpcsL_mean[1,1]) `star_cog_cogU_cpcsL' " & " %02.0f ( n_cog_u_cpcs_l ) " & " %02.0f ( n_clust_cog_u_cpcs_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogU_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & Math Bottom 50\%  & CPCS Top 50\% & " %04.3f (cog_cogL_cpcsU_mean[1,1]) `star_cog_cogL_cpcsU' " & " %02.0f ( n_cog_l_cpcs_u ) " & " %02.0f ( n_clust_cog_l_cpcs_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogL_cpcsU_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cog_cogL_cpcsL_mean[1,1]) `star_cog_cogL_cpcsL' " & " %02.0f ( n_cog_l_cpcs_l ) " & " %02.0f ( n_clust_cog_l_cpcs_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogL_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. 
. file write `hh2' " CPCS score^{c -(}a{c )-} & Math Top 50\%  & CPCS Top 50\% & " %04.3f (cpcs_cogU_cpcsU_mean[1,1]) `star_cpcs_cogU_cpcsU' " & " %02.0f ( n_cpcs_u_cog_u ) " & " %02.0f ( n_clust_cpcs_u_cog_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cpcs_cogU_cpcsU_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cpcs_cogU_cpcsL_mean[1,1]) `star_cpcs_cogU_cpcsL' " & " %02.0f ( n_cpcs_l_cog_u ) " & " %02.0f ( n_clust_cpcs_l_cog_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &       & & ( " %04.3f (cpcs_cogU_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & Math Bottom 50\%  & CPCS Top 50\% & " %04.3f (cpcs_cogL_cpcsU_mean[1,1]) `star_cpcs_cogL_cpcsU' " & " %02.0f ( n_cpcs_u_cog_l ) " & " %02.0f ( n_clust_cpcs_u_cog_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cpcs_cogL_cpcsU_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cpcs_cogL_cpcsL_mean[1,1]) `star_cpcs_cogL_cpcsL' " & " %02.0f ( n_cpcs_l_cog_l ) " & " %02.0f ( n_clust_cpcs_l_cog_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &       & & ( " %04.3f (cpcs_cogL_cpcsL_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. 
. file write `hh2' "\\\midrule" _newline
{txt}
{com}. file write `hh2' "\end{c -(}tabular{c )-}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\item (a) Dependent variables are standardized using the average and variance of the whole follow-up sample in the February 2022 survey. " _newline
{txt}
{com}. file write `hh2' "\item (b) Cutoffs are created based on whether their ability to perform each item at the baseline was higher or lower than the median. " _newline
{txt}
{com}. file write `hh2' "\item (c) Wild cluster bootstrap p-values are reported within parentheses. Clusters are schools at the baseline." _newline
{txt}
{com}. file write `hh2' "\item (d) $^*$ Significant at 10\% level; $^{c -(}**{c )-}$ significant at 5\% level; $^{c -(}***{c )-}$ significant at 1\% level. " _newline
{txt}
{com}. file write `hh2' "\end{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}threeparttable{c )-}" _newline
{txt}
{com}. file write `hh2' "{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:addlabel{c )-}%" _newline
{txt}
{com}. file write `hh2' "\end{c -(}table{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. file close `hh2'
{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/mb/s9qljd594gbfyhl2dqnnwlcw0000gn/T//SD56186.000000"
{txt}
{com}. * This is the do file to create "Table 5. Heterogeneity by Baseline Abilites (Math and CPCS)"
. set seed 111
{txt}
{com}. 
. use "$path_data/temp/followup_student_baseline_score_missing_dummy", replace
{txt}
{com}. 
. egen DT_score_pre_std_med = median(DT_score_pre_std)
{txt}
{com}. gen DT_score_pre_std_upper50 = 1 if DT_score_pre_std>DT_score_pre_std_med
{txt}(121 missing values generated)

{com}. recode DT_score_pre_std_upper50 (.=0)
{txt}(121 changes made to {bf:DT_score_pre_std_upper50})

{com}. 
. egen rosen_pre_std_med = median(rosen_pre_std)
{txt}
{com}. gen rosen_pre_std_upper50 = 1 if rosen_pre_std>rosen_pre_std_med
{txt}(128 missing values generated)

{com}. recode rosen_pre_std_upper50 (.=0)
{txt}(128 changes made to {bf:rosen_pre_std_upper50})

{com}. rename rosen_pre_std_upper50 RSES_std_upper50
{res}{txt}
{com}. 
. egen cpcs_pre_std_med = median(cpcs_pre_std)
{txt}
{com}. gen cpcs_pre_std_upper50 = 1 if cpcs_pre_std>cpcs_pre_std_med
{txt}(135 missing values generated)

{com}. recode cpcs_pre_std_upper50 (.=0)
{txt}(135 changes made to {bf:cpcs_pre_std_upper50})

{com}. rename cpcs_pre_std_upper50 CPCS_std_upper50
{res}{txt}
{com}. 
. 
. /// Cognitive
> wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 59
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 22
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2397567{col 38}{space 1}  -0.86{col 46}{space 3}0.418{col 54}{space 3}-.7966316{col 66}{space 3}  .434311
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_rses_u = e(N)
{txt}
{com}. scalar n_clust_cog_u_rses_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.3459862{col 38}{space 1}  -1.14{col 46}{space 3}0.274{col 54}{space 3}-1.046262{col 66}{space 3}   .30228
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_rses_l = e(N)
{txt}
{com}. scalar n_clust_cog_u_rses_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2419353{col 38}{space 1}  -0.87{col 46}{space 3}0.364{col 54}{space 3}-.8619956{col 66}{space 3} .3835373
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_rses_u = e(N)
{txt}
{com}. scalar n_clust_cog_l_rses_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0781293{col 38}{space 1}  -0.29{col 46}{space 3}0.764{col 54}{space 3}-.6361322{col 66}{space 3} .4379878
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_rses_l = e(N)
{txt}
{com}. scalar n_clust_cog_l_rses_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:29})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 55
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0533319{col 38}{space 1}   0.17{col 46}{space 3}0.826{col 54}{space 3}-.6512296{col 66}{space 3} .8167328
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_cpcs_u = e(N)
{txt}
{com}. scalar n_clust_cog_u_cpcs_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 67
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.5660393{col 38}{space 1}  -1.84{col 46}{space 3}0.098{col 54}{space 3}-1.240136{col 66}{space 3}  .119599
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_cpcs_l = e(N)
{txt}
{com}. scalar n_clust_cog_u_cpcs_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0295186{col 38}{space 1}   0.10{col 46}{space 3}0.898{col 54}{space 3}-.5958441{col 66}{space 3} .6626134
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_cpcs_u = e(N)
{txt}
{com}. scalar n_clust_cog_l_cpcs_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:19})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 68
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  -.19243{col 38}{space 1}  -0.73{col 46}{space 3}0.482{col 54}{space 3}-.7246009{col 66}{space 3} .3950123
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_cpcs_l = e(N)
{txt}
{com}. scalar n_clust_cog_l_cpcs_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. 
. 
. /// RSES
> wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .9579135{col 38}{space 1}   3.36{col 46}{space 3}0.016{col 54}{space 3}   .26476{col 66}{space 3} 1.654666
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_u_cog_u = e(N)
{txt}
{com}. scalar n_clust_rses_u_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 61
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0615952{col 38}{space 1}   0.32{col 46}{space 3}0.746{col 54}{space 3}-.3207408{col 66}{space 3} .4426532
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_l_cog_u = e(N)
{txt}
{com}. scalar n_clust_rses_l_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .4920182{col 38}{space 1}   1.44{col 46}{space 3}0.206{col 54}{space 3}-.3791899{col 66}{space 3} 1.193207
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_u_cog_l = e(N)
{txt}
{com}. scalar n_clust_rses_u_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1388312{col 38}{space 1}   0.42{col 46}{space 3}0.678{col 54}{space 3}-.6031055{col 66}{space 3}  .913588
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_l_cog_l = e(N)
{txt}
{com}. scalar n_clust_rses_l_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:19})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 52
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 20
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .6664341{col 38}{space 1}   3.26{col 46}{space 3}0.012{col 54}{space 3} .2321136{col 66}{space 3} 1.106288
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3847293{col 38}{space 1}   1.53{col 46}{space 3}0.124{col 54}{space 3}-.1218257{col 66}{space 3} .9801306
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2062219{col 38}{space 1}   0.47{col 46}{space 3}0.686{col 54}{space 3}-1.027415{col 66}{space 3} 1.180279
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 66
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3332931{col 38}{space 1}   0.96{col 46}{space 3}0.380{col 54}{space 3}-.4877823{col 66}{space 3} 1.105785
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. 
. 
. /// CPCS
> 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} 1.020132{col 38}{space 1}   3.66{col 46}{space 3}0.006{col 54}{space 3} .3600309{col 66}{space 3} 1.627818
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 61
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0665639{col 38}{space 1}   0.34{col 46}{space 3}0.810{col 54}{space 3}-.3876734{col 66}{space 3} .4535088
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .5458399{col 38}{space 1}   1.63{col 46}{space 3}0.144{col 54}{space 3}-.2669703{col 66}{space 3} 1.253775
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1431476{col 38}{space 1}   0.41{col 46}{space 3}0.704{col 54}{space 3}-.6225786{col 66}{space 3} 1.047643
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 52
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 20
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  .774167{col 38}{space 1}   3.68{col 46}{space 3}0.002{col 54}{space 3} .3283526{col 66}{space 3} 1.235857
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_u_cog_u = e(N)
{txt}
{com}. scalar n_clust_cpcs_u_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3575321{col 38}{space 1}   1.51{col 46}{space 3}0.162{col 54}{space 3}-.1390679{col 66}{space 3} .8784332
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_l_cog_u = e(N)
{txt}
{com}. scalar n_clust_cpcs_l_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2792052{col 38}{space 1}   0.63{col 46}{space 3}0.554{col 54}{space 3}-.8321789{col 66}{space 3} 1.362015
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_u_cog_l = e(N)
{txt}
{com}. scalar n_clust_cpcs_u_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 66
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}   .33148{col 38}{space 1}   0.95{col 46}{space 3}0.352{col 54}{space 3}-.4706789{col 66}{space 3} 1.158291
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_l_cog_l = e(N)
{txt}
{com}. scalar n_clust_cpcs_l_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. // significant level
. 
. 
. local outcome cog rses cpcs
{txt}
{com}. local hetero1 cogU cogL
{txt}
{com}. local hetero2 rsesU rsesL
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. foreach h1 in `hetero1'{c -(}
{txt}  3{com}. foreach h2 in `hetero2'{c -(}
{txt}  4{com}.                 if `dep'_`h1'_`h2'_pv[1,1]<=0.01 {c -(}
{txt}  5{com}.                         local star_`dep'_`h1'_`h2' %3s "***"
{txt}  6{com}.                 {c )-}
{txt}  7{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.01) & (`dep'_`h1'_`h2'_pv[1,1]<=0.05) {c -(}
{txt}  8{com}.                         local star_`dep'_`h1'_`h2' %2s "**"
{txt}  9{com}.                 {c )-}
{txt} 10{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.05) & (`dep'_`h1'_`h2'_pv[1,1]<=0.10) {c -(}
{txt} 11{com}.                         local star_`dep'_`h1'_`h2' %1s "*"
{txt} 12{com}.                 {c )-}
{txt} 13{com}.                 else {c -(}
{txt} 14{com}.                         local star_`dep'_`h1'_`h2'  ""
{txt} 15{com}.                 {c )-}
{txt} 16{com}. {c )-} 
{txt} 17{com}. {c )-}
{txt} 18{com}. {c )-}
{txt}
{com}. 
. local outcome cog rses cpcs
{txt}
{com}. local hetero1 cogU cogL
{txt}
{com}. local hetero2 cpcsU cpcsL
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. foreach h1 in `hetero1'{c -(}
{txt}  3{com}. foreach h2 in `hetero2'{c -(}
{txt}  4{com}.                 if `dep'_`h1'_`h2'_pv[1,1]<=0.01 {c -(}
{txt}  5{com}.                         local star_`dep'_`h1'_`h2' %3s "***"
{txt}  6{com}.                 {c )-}
{txt}  7{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.01) & (`dep'_`h1'_`h2'_pv[1,1]<=0.05) {c -(}
{txt}  8{com}.                         local star_`dep'_`h1'_`h2' %2s "**"
{txt}  9{com}.                 {c )-}
{txt} 10{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.05) & (`dep'_`h1'_`h2'_pv[1,1]<=0.10) {c -(}
{txt} 11{com}.                         local star_`dep'_`h1'_`h2' %1s "*"
{txt} 12{com}.                 {c )-}
{txt} 13{com}.                 else {c -(}
{txt} 14{com}.                         local star_`dep'_`h1'_`h2'  ""
{txt} 15{com}.                 {c )-}
{txt} 16{com}. {c )-} 
{txt} 17{com}. {c )-}
{txt} 18{com}. {c )-}
{txt}
{com}. 
. /// Table
> 
. tempname hh2
{txt}
{com}. file open `hh2' using "$path_output/hetero_2by2_CPCS.tex", write replace
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "% Author: Kazuma Takakura" _newline
{txt}
{com}. file write `hh2' "% Date: `c(current_date)'" _newline
{txt}
{com}. file write `hh2' "% Time: `c(current_time)'" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. 
. file write `hh2' "\begin{c -(}table{c )-}[h!]\footnotesize" _newline
{txt}
{com}. file write `hh2' "  \centering" _newline
{txt}
{com}. file write `hh2' "  \caption{c -(}Heterogeneity among Baseline Abilites (Math and CPCS){c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:hetero_cpcs{c )-}" _newline
{txt}
{com}. file write `hh2' "\scalebox{c -(}1{c )-}{c -(}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}threeparttable{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "\begin{c -(}tabular{c )-}{c -(}lccccccc{c )-}\toprule" _newline
{txt}
{com}. 
. file write `hh2' " Dependent Variable & \multicolumn{c -(}2{c )-}{c -(}c{c )-}{c -(}Baseline^{c -(}b{c )-}{c )-} & Difference & Obs & N of clusters  \\\midrule\midrule" _newline
{txt}
{com}. file write `hh2' " Rapid math test score^{c -(}a{c )-} & Math Top 50\%  & CPCS Top 50\% & " %04.3f (cog_cogU_rsesU_mean[1,1]) `star_cog_cogU_cpcsU' " & " %02.0f ( n_cog_u_cpcs_u ) " & " %02.0f ( n_clust_cog_u_cpcs_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogU_cpcsU_pv[1,1]) " )  & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cog_cogU_cpcsL_mean[1,1]) `star_cog_cogU_cpcsL' " & " %02.0f ( n_cog_u_cpcs_l ) " & " %02.0f ( n_clust_cog_u_cpcs_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogU_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & Math Bottom 50\%  & CPCS Top 50\% & " %04.3f (cog_cogL_cpcsU_mean[1,1]) `star_cog_cogL_cpcsU' " & " %02.0f ( n_cog_l_cpcs_u ) " & " %02.0f ( n_clust_cog_l_cpcs_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogL_cpcsU_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cog_cogL_cpcsL_mean[1,1]) `star_cog_cogL_cpcsL' " & " %02.0f ( n_cog_l_cpcs_l ) " & " %02.0f ( n_clust_cog_l_cpcs_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogL_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. 
. file write `hh2' " CPCS score^{c -(}a{c )-} & Math Top 50\%  & CPCS Top 50\% & " %04.3f (cpcs_cogU_cpcsU_mean[1,1]) `star_cpcs_cogU_cpcsU' " & " %02.0f ( n_cpcs_u_cog_u ) " & " %02.0f ( n_clust_cpcs_u_cog_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cpcs_cogU_cpcsU_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cpcs_cogU_cpcsL_mean[1,1]) `star_cpcs_cogU_cpcsL' " & " %02.0f ( n_cpcs_l_cog_u ) " & " %02.0f ( n_clust_cpcs_l_cog_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &       & & ( " %04.3f (cpcs_cogU_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & Math Bottom 50\%  & CPCS Top 50\% & " %04.3f (cpcs_cogL_cpcsU_mean[1,1]) `star_cpcs_cogL_cpcsU' " & " %02.0f ( n_cpcs_u_cog_l ) " & " %02.0f ( n_clust_cpcs_u_cog_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cpcs_cogL_cpcsU_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cpcs_cogL_cpcsL_mean[1,1]) `star_cpcs_cogL_cpcsL' " & " %02.0f ( n_cpcs_l_cog_l ) " & " %02.0f ( n_clust_cpcs_l_cog_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &       & & ( " %04.3f (cpcs_cogL_cpcsL_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. 
. file write `hh2' "\\\midrule" _newline
{txt}
{com}. file write `hh2' "\end{c -(}tabular{c )-}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\item (a) Dependent variables are standardized using the average and variance of the whole follow-up sample in the February 2022 survey. " _newline
{txt}
{com}. file write `hh2' "\item (b) Cutoffs are created based on whether their ability to perform each item at the baseline was higher or lower than the median. " _newline
{txt}
{com}. file write `hh2' "\item (c) Wild cluster bootstrap p-values are reported within parentheses. Clusters are schools at the baseline." _newline
{txt}
{com}. file write `hh2' "\item (d) $^*$ Significant at 10\% level; $^{c -(}**{c )-}$ significant at 5\% level; $^{c -(}***{c )-}$ significant at 1\% level. " _newline
{txt}
{com}. file write `hh2' "\end{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}threeparttable{c )-}" _newline
{txt}
{com}. file write `hh2' "{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:addlabel{c )-}%" _newline
{txt}
{com}. file write `hh2' "\end{c -(}table{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. file close `hh2'
{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/mb/s9qljd594gbfyhl2dqnnwlcw0000gn/T//SD56186.000000"
{txt}
{com}. * This is the do file to create "Table 5. Heterogeneity by Baseline Abilites (Math and CPCS)"
. set seed 1111
{txt}
{com}. 
. use "$path_data/temp/followup_student_baseline_score_missing_dummy", replace
{txt}
{com}. 
. egen DT_score_pre_std_med = median(DT_score_pre_std)
{txt}
{com}. gen DT_score_pre_std_upper50 = 1 if DT_score_pre_std>DT_score_pre_std_med
{txt}(121 missing values generated)

{com}. recode DT_score_pre_std_upper50 (.=0)
{txt}(121 changes made to {bf:DT_score_pre_std_upper50})

{com}. 
. egen rosen_pre_std_med = median(rosen_pre_std)
{txt}
{com}. gen rosen_pre_std_upper50 = 1 if rosen_pre_std>rosen_pre_std_med
{txt}(128 missing values generated)

{com}. recode rosen_pre_std_upper50 (.=0)
{txt}(128 changes made to {bf:rosen_pre_std_upper50})

{com}. rename rosen_pre_std_upper50 RSES_std_upper50
{res}{txt}
{com}. 
. egen cpcs_pre_std_med = median(cpcs_pre_std)
{txt}
{com}. gen cpcs_pre_std_upper50 = 1 if cpcs_pre_std>cpcs_pre_std_med
{txt}(135 missing values generated)

{com}. recode cpcs_pre_std_upper50 (.=0)
{txt}(135 changes made to {bf:cpcs_pre_std_upper50})

{com}. rename cpcs_pre_std_upper50 CPCS_std_upper50
{res}{txt}
{com}. 
. 
. /// Cognitive
> wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 59
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 22
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2397567{col 38}{space 1}  -0.86{col 46}{space 3}0.438{col 54}{space 3}-.8084687{col 66}{space 3} .4069068
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_rses_u = e(N)
{txt}
{com}. scalar n_clust_cog_u_rses_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.3459862{col 38}{space 1}  -1.14{col 46}{space 3}0.242{col 54}{space 3}-1.021292{col 66}{space 3} .3479614
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_rses_l = e(N)
{txt}
{com}. scalar n_clust_cog_u_rses_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2419353{col 38}{space 1}  -0.87{col 46}{space 3}0.458{col 54}{space 3}-.8886186{col 66}{space 3} .4312118
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_rses_u = e(N)
{txt}
{com}. scalar n_clust_cog_l_rses_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0781293{col 38}{space 1}  -0.29{col 46}{space 3}0.790{col 54}{space 3}-.6038098{col 66}{space 3} .5255066
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_rses_l = e(N)
{txt}
{com}. scalar n_clust_cog_l_rses_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 55
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0533319{col 38}{space 1}   0.17{col 46}{space 3}0.842{col 54}{space 3}-.6204316{col 66}{space 3} .8102234
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_cpcs_u = e(N)
{txt}
{com}. scalar n_clust_cog_u_cpcs_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 67
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.5660393{col 38}{space 1}  -1.84{col 46}{space 3}0.090{col 54}{space 3}-1.183374{col 66}{space 3} .1191932
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_cpcs_l = e(N)
{txt}
{com}. scalar n_clust_cog_u_cpcs_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0295186{col 38}{space 1}   0.10{col 46}{space 3}0.884{col 54}{space 3}-.6082701{col 66}{space 3}  .801894
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_cpcs_u = e(N)
{txt}
{com}. scalar n_clust_cog_l_cpcs_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 68
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  -.19243{col 38}{space 1}  -0.73{col 46}{space 3}0.448{col 54}{space 3} -.765462{col 66}{space 3} .3429002
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_cpcs_l = e(N)
{txt}
{com}. scalar n_clust_cog_l_cpcs_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. 
. 
. /// RSES
> wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .9579135{col 38}{space 1}   3.36{col 46}{space 3}0.010{col 54}{space 3} .2787565{col 66}{space 3}  1.62041
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_u_cog_u = e(N)
{txt}
{com}. scalar n_clust_rses_u_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 61
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0615952{col 38}{space 1}   0.32{col 46}{space 3}0.766{col 54}{space 3} -.397067{col 66}{space 3} .4088516
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_l_cog_u = e(N)
{txt}
{com}. scalar n_clust_rses_l_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .4920182{col 38}{space 1}   1.44{col 46}{space 3}0.194{col 54}{space 3}-.2628764{col 66}{space 3} 1.175857
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_u_cog_l = e(N)
{txt}
{com}. scalar n_clust_rses_u_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1388312{col 38}{space 1}   0.42{col 46}{space 3}0.624{col 54}{space 3}-.5883907{col 66}{space 3} .8934127
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_l_cog_l = e(N)
{txt}
{com}. scalar n_clust_rses_l_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 52
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 20
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .6664341{col 38}{space 1}   3.26{col 46}{space 3}0.002{col 54}{space 3} .2055946{col 66}{space 3}  1.14356
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3847293{col 38}{space 1}   1.53{col 46}{space 3}0.146{col 54}{space 3}-.1643196{col 66}{space 3} .9509508
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2062219{col 38}{space 1}   0.47{col 46}{space 3}0.602{col 54}{space 3}-.8201526{col 66}{space 3} 1.180365
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 66
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3332931{col 38}{space 1}   0.96{col 46}{space 3}0.410{col 54}{space 3}-.4739231{col 66}{space 3} 1.063097
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. 
. 
. /// CPCS
> 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} 1.020132{col 38}{space 1}   3.66{col 46}{space 3}0.002{col 54}{space 3} .4189367{col 66}{space 3} 1.690029
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 61
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0665639{col 38}{space 1}   0.34{col 46}{space 3}0.742{col 54}{space 3}-.3967434{col 66}{space 3} .5061282
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .5458399{col 38}{space 1}   1.63{col 46}{space 3}0.150{col 54}{space 3}-.2496979{col 66}{space 3} 1.250735
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1431476{col 38}{space 1}   0.41{col 46}{space 3}0.704{col 54}{space 3}-.7431742{col 66}{space 3} .9775981
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 52
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 20
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  .774167{col 38}{space 1}   3.68{col 46}{space 3}0.002{col 54}{space 3} .3336309{col 66}{space 3} 1.253487
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_u_cog_u = e(N)
{txt}
{com}. scalar n_clust_cpcs_u_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3575321{col 38}{space 1}   1.51{col 46}{space 3}0.142{col 54}{space 3}-.1495744{col 66}{space 3} .9198072
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_l_cog_u = e(N)
{txt}
{com}. scalar n_clust_cpcs_l_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2792052{col 38}{space 1}   0.63{col 46}{space 3}0.568{col 54}{space 3}-.8609089{col 66}{space 3} 1.260092
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_u_cog_l = e(N)
{txt}
{com}. scalar n_clust_cpcs_u_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 66
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}   .33148{col 38}{space 1}   0.95{col 46}{space 3}0.418{col 54}{space 3}-.4964017{col 66}{space 3} 1.053111
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_l_cog_l = e(N)
{txt}
{com}. scalar n_clust_cpcs_l_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. // significant level
. 
. 
. local outcome cog rses cpcs
{txt}
{com}. local hetero1 cogU cogL
{txt}
{com}. local hetero2 rsesU rsesL
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. foreach h1 in `hetero1'{c -(}
{txt}  3{com}. foreach h2 in `hetero2'{c -(}
{txt}  4{com}.                 if `dep'_`h1'_`h2'_pv[1,1]<=0.01 {c -(}
{txt}  5{com}.                         local star_`dep'_`h1'_`h2' %3s "***"
{txt}  6{com}.                 {c )-}
{txt}  7{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.01) & (`dep'_`h1'_`h2'_pv[1,1]<=0.05) {c -(}
{txt}  8{com}.                         local star_`dep'_`h1'_`h2' %2s "**"
{txt}  9{com}.                 {c )-}
{txt} 10{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.05) & (`dep'_`h1'_`h2'_pv[1,1]<=0.10) {c -(}
{txt} 11{com}.                         local star_`dep'_`h1'_`h2' %1s "*"
{txt} 12{com}.                 {c )-}
{txt} 13{com}.                 else {c -(}
{txt} 14{com}.                         local star_`dep'_`h1'_`h2'  ""
{txt} 15{com}.                 {c )-}
{txt} 16{com}. {c )-} 
{txt} 17{com}. {c )-}
{txt} 18{com}. {c )-}
{txt}
{com}. 
. local outcome cog rses cpcs
{txt}
{com}. local hetero1 cogU cogL
{txt}
{com}. local hetero2 cpcsU cpcsL
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. foreach h1 in `hetero1'{c -(}
{txt}  3{com}. foreach h2 in `hetero2'{c -(}
{txt}  4{com}.                 if `dep'_`h1'_`h2'_pv[1,1]<=0.01 {c -(}
{txt}  5{com}.                         local star_`dep'_`h1'_`h2' %3s "***"
{txt}  6{com}.                 {c )-}
{txt}  7{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.01) & (`dep'_`h1'_`h2'_pv[1,1]<=0.05) {c -(}
{txt}  8{com}.                         local star_`dep'_`h1'_`h2' %2s "**"
{txt}  9{com}.                 {c )-}
{txt} 10{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.05) & (`dep'_`h1'_`h2'_pv[1,1]<=0.10) {c -(}
{txt} 11{com}.                         local star_`dep'_`h1'_`h2' %1s "*"
{txt} 12{com}.                 {c )-}
{txt} 13{com}.                 else {c -(}
{txt} 14{com}.                         local star_`dep'_`h1'_`h2'  ""
{txt} 15{com}.                 {c )-}
{txt} 16{com}. {c )-} 
{txt} 17{com}. {c )-}
{txt} 18{com}. {c )-}
{txt}
{com}. 
. /// Table
> 
. tempname hh2
{txt}
{com}. file open `hh2' using "$path_output/hetero_2by2_CPCS.tex", write replace
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "% Author: Kazuma Takakura" _newline
{txt}
{com}. file write `hh2' "% Date: `c(current_date)'" _newline
{txt}
{com}. file write `hh2' "% Time: `c(current_time)'" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. 
. file write `hh2' "\begin{c -(}table{c )-}[h!]\footnotesize" _newline
{txt}
{com}. file write `hh2' "  \centering" _newline
{txt}
{com}. file write `hh2' "  \caption{c -(}Heterogeneity among Baseline Abilites (Math and CPCS){c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:hetero_cpcs{c )-}" _newline
{txt}
{com}. file write `hh2' "\scalebox{c -(}1{c )-}{c -(}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}threeparttable{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "\begin{c -(}tabular{c )-}{c -(}lccccccc{c )-}\toprule" _newline
{txt}
{com}. 
. file write `hh2' " Dependent Variable & \multicolumn{c -(}2{c )-}{c -(}c{c )-}{c -(}Baseline^{c -(}b{c )-}{c )-} & Difference & Obs & N of clusters  \\\midrule\midrule" _newline
{txt}
{com}. file write `hh2' " Rapid math test score^{c -(}a{c )-} & Math Top 50\%  & CPCS Top 50\% & " %04.3f (cog_cogU_rsesU_mean[1,1]) `star_cog_cogU_cpcsU' " & " %02.0f ( n_cog_u_cpcs_u ) " & " %02.0f ( n_clust_cog_u_cpcs_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogU_cpcsU_pv[1,1]) " )  & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cog_cogU_cpcsL_mean[1,1]) `star_cog_cogU_cpcsL' " & " %02.0f ( n_cog_u_cpcs_l ) " & " %02.0f ( n_clust_cog_u_cpcs_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogU_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & Math Bottom 50\%  & CPCS Top 50\% & " %04.3f (cog_cogL_cpcsU_mean[1,1]) `star_cog_cogL_cpcsU' " & " %02.0f ( n_cog_l_cpcs_u ) " & " %02.0f ( n_clust_cog_l_cpcs_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogL_cpcsU_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cog_cogL_cpcsL_mean[1,1]) `star_cog_cogL_cpcsL' " & " %02.0f ( n_cog_l_cpcs_l ) " & " %02.0f ( n_clust_cog_l_cpcs_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogL_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. 
. file write `hh2' " CPCS score^{c -(}a{c )-} & Math Top 50\%  & CPCS Top 50\% & " %04.3f (cpcs_cogU_cpcsU_mean[1,1]) `star_cpcs_cogU_cpcsU' " & " %02.0f ( n_cpcs_u_cog_u ) " & " %02.0f ( n_clust_cpcs_u_cog_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cpcs_cogU_cpcsU_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cpcs_cogU_cpcsL_mean[1,1]) `star_cpcs_cogU_cpcsL' " & " %02.0f ( n_cpcs_l_cog_u ) " & " %02.0f ( n_clust_cpcs_l_cog_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &       & & ( " %04.3f (cpcs_cogU_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & Math Bottom 50\%  & CPCS Top 50\% & " %04.3f (cpcs_cogL_cpcsU_mean[1,1]) `star_cpcs_cogL_cpcsU' " & " %02.0f ( n_cpcs_u_cog_l ) " & " %02.0f ( n_clust_cpcs_u_cog_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cpcs_cogL_cpcsU_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cpcs_cogL_cpcsL_mean[1,1]) `star_cpcs_cogL_cpcsL' " & " %02.0f ( n_cpcs_l_cog_l ) " & " %02.0f ( n_clust_cpcs_l_cog_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &       & & ( " %04.3f (cpcs_cogL_cpcsL_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. 
. file write `hh2' "\\\midrule" _newline
{txt}
{com}. file write `hh2' "\end{c -(}tabular{c )-}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\item (a) Dependent variables are standardized using the average and variance of the whole follow-up sample in the February 2022 survey. " _newline
{txt}
{com}. file write `hh2' "\item (b) Cutoffs are created based on whether their ability to perform each item at the baseline was higher or lower than the median. " _newline
{txt}
{com}. file write `hh2' "\item (c) Wild cluster bootstrap p-values are reported within parentheses. Clusters are schools at the baseline." _newline
{txt}
{com}. file write `hh2' "\item (d) $^*$ Significant at 10\% level; $^{c -(}**{c )-}$ significant at 5\% level; $^{c -(}***{c )-}$ significant at 1\% level. " _newline
{txt}
{com}. file write `hh2' "\end{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}threeparttable{c )-}" _newline
{txt}
{com}. file write `hh2' "{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:addlabel{c )-}%" _newline
{txt}
{com}. file write `hh2' "\end{c -(}table{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. file close `hh2'
{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/mb/s9qljd594gbfyhl2dqnnwlcw0000gn/T//SD56186.000000"
{txt}
{com}. * This is the do file to create "Table 5. Heterogeneity by Baseline Abilites (Math and CPCS)"
. set seed 11111
{txt}
{com}. 
. use "$path_data/temp/followup_student_baseline_score_missing_dummy", replace
{txt}
{com}. 
. egen DT_score_pre_std_med = median(DT_score_pre_std)
{txt}
{com}. gen DT_score_pre_std_upper50 = 1 if DT_score_pre_std>DT_score_pre_std_med
{txt}(121 missing values generated)

{com}. recode DT_score_pre_std_upper50 (.=0)
{txt}(121 changes made to {bf:DT_score_pre_std_upper50})

{com}. 
. egen rosen_pre_std_med = median(rosen_pre_std)
{txt}
{com}. gen rosen_pre_std_upper50 = 1 if rosen_pre_std>rosen_pre_std_med
{txt}(128 missing values generated)

{com}. recode rosen_pre_std_upper50 (.=0)
{txt}(128 changes made to {bf:rosen_pre_std_upper50})

{com}. rename rosen_pre_std_upper50 RSES_std_upper50
{res}{txt}
{com}. 
. egen cpcs_pre_std_med = median(cpcs_pre_std)
{txt}
{com}. gen cpcs_pre_std_upper50 = 1 if cpcs_pre_std>cpcs_pre_std_med
{txt}(135 missing values generated)

{com}. recode cpcs_pre_std_upper50 (.=0)
{txt}(135 changes made to {bf:cpcs_pre_std_upper50})

{com}. rename cpcs_pre_std_upper50 CPCS_std_upper50
{res}{txt}
{com}. 
. 
. /// Cognitive
> wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 59
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 22
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2397567{col 38}{space 1}  -0.86{col 46}{space 3}0.442{col 54}{space 3}-.8406214{col 66}{space 3} .4161941
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_rses_u = e(N)
{txt}
{com}. scalar n_clust_cog_u_rses_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.3459862{col 38}{space 1}  -1.14{col 46}{space 3}0.272{col 54}{space 3}-.9390312{col 66}{space 3} .2653954
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_rses_l = e(N)
{txt}
{com}. scalar n_clust_cog_u_rses_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2419353{col 38}{space 1}  -0.87{col 46}{space 3}0.376{col 54}{space 3}-.8813633{col 66}{space 3} .3970286
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_rses_u = e(N)
{txt}
{com}. scalar n_clust_cog_l_rses_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0781293{col 38}{space 1}  -0.29{col 46}{space 3}0.782{col 54}{space 3} -.633589{col 66}{space 3} .5514957
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_rses_l = e(N)
{txt}
{com}. scalar n_clust_cog_l_rses_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 55
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0533319{col 38}{space 1}   0.17{col 46}{space 3}0.882{col 54}{space 3}-.6611625{col 66}{space 3}  .806006
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_cpcs_u = e(N)
{txt}
{com}. scalar n_clust_cog_u_cpcs_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 67
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.5660393{col 38}{space 1}  -1.84{col 46}{space 3}0.106{col 54}{space 3}-1.193385{col 66}{space 3} .1454137
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_cpcs_l = e(N)
{txt}
{com}. scalar n_clust_cog_u_cpcs_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0295186{col 38}{space 1}   0.10{col 46}{space 3}0.894{col 54}{space 3}-.5566965{col 66}{space 3} .7313526
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_cpcs_u = e(N)
{txt}
{com}. scalar n_clust_cog_l_cpcs_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 68
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  -.19243{col 38}{space 1}  -0.73{col 46}{space 3}0.530{col 54}{space 3}-.7624104{col 66}{space 3} .3217706
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_cpcs_l = e(N)
{txt}
{com}. scalar n_clust_cog_l_cpcs_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. 
. 
. /// RSES
> wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .9579135{col 38}{space 1}   3.36{col 46}{space 3}0.006{col 54}{space 3} .2995461{col 66}{space 3}  1.61595
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_u_cog_u = e(N)
{txt}
{com}. scalar n_clust_rses_u_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 61
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0615952{col 38}{space 1}   0.32{col 46}{space 3}0.772{col 54}{space 3}-.3396273{col 66}{space 3} .4674283
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_l_cog_u = e(N)
{txt}
{com}. scalar n_clust_rses_l_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .4920182{col 38}{space 1}   1.44{col 46}{space 3}0.196{col 54}{space 3}-.3089123{col 66}{space 3} 1.207425
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_u_cog_l = e(N)
{txt}
{com}. scalar n_clust_rses_u_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1388312{col 38}{space 1}   0.42{col 46}{space 3}0.754{col 54}{space 3}-.6694186{col 66}{space 3}   .96379
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_l_cog_l = e(N)
{txt}
{com}. scalar n_clust_rses_l_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 52
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 20
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .6664341{col 38}{space 1}   3.26{col 46}{space 3}0.006{col 54}{space 3} .1980499{col 66}{space 3} 1.114933
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3847293{col 38}{space 1}   1.53{col 46}{space 3}0.132{col 54}{space 3} -.142619{col 66}{space 3} .9763969
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2062219{col 38}{space 1}   0.47{col 46}{space 3}0.648{col 54}{space 3}-.9251009{col 66}{space 3} 1.214517
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 66
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3332931{col 38}{space 1}   0.96{col 46}{space 3}0.396{col 54}{space 3}-.5699815{col 66}{space 3}  1.19557
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. 
. 
. /// CPCS
> 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} 1.020132{col 38}{space 1}   3.66{col 46}{space 3}0.006{col 54}{space 3} .4028772{col 66}{space 3}  1.72851
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 61
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0665639{col 38}{space 1}   0.34{col 46}{space 3}0.684{col 54}{space 3} -.332363{col 66}{space 3} .4531203
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:19})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .5458399{col 38}{space 1}   1.63{col 46}{space 3}0.150{col 54}{space 3}-.3241352{col 66}{space 3} 1.232792
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1431476{col 38}{space 1}   0.41{col 46}{space 3}0.738{col 54}{space 3}-.6865997{col 66}{space 3} 1.002436
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 52
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 20
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  .774167{col 38}{space 1}   3.68{col 46}{space 3}0.002{col 54}{space 3} .3584126{col 66}{space 3} 1.254922
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_u_cog_u = e(N)
{txt}
{com}. scalar n_clust_cpcs_u_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3575321{col 38}{space 1}   1.51{col 46}{space 3}0.148{col 54}{space 3}-.1602723{col 66}{space 3}  .881994
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_l_cog_u = e(N)
{txt}
{com}. scalar n_clust_cpcs_l_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2792052{col 38}{space 1}   0.63{col 46}{space 3}0.514{col 54}{space 3}-.8447186{col 66}{space 3} 1.281683
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_u_cog_l = e(N)
{txt}
{com}. scalar n_clust_cpcs_u_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 66
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}   .33148{col 38}{space 1}   0.95{col 46}{space 3}0.408{col 54}{space 3}-.4831461{col 66}{space 3} 1.121361
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_l_cog_l = e(N)
{txt}
{com}. scalar n_clust_cpcs_l_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. // significant level
. 
. 
. local outcome cog rses cpcs
{txt}
{com}. local hetero1 cogU cogL
{txt}
{com}. local hetero2 rsesU rsesL
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. foreach h1 in `hetero1'{c -(}
{txt}  3{com}. foreach h2 in `hetero2'{c -(}
{txt}  4{com}.                 if `dep'_`h1'_`h2'_pv[1,1]<=0.01 {c -(}
{txt}  5{com}.                         local star_`dep'_`h1'_`h2' %3s "***"
{txt}  6{com}.                 {c )-}
{txt}  7{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.01) & (`dep'_`h1'_`h2'_pv[1,1]<=0.05) {c -(}
{txt}  8{com}.                         local star_`dep'_`h1'_`h2' %2s "**"
{txt}  9{com}.                 {c )-}
{txt} 10{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.05) & (`dep'_`h1'_`h2'_pv[1,1]<=0.10) {c -(}
{txt} 11{com}.                         local star_`dep'_`h1'_`h2' %1s "*"
{txt} 12{com}.                 {c )-}
{txt} 13{com}.                 else {c -(}
{txt} 14{com}.                         local star_`dep'_`h1'_`h2'  ""
{txt} 15{com}.                 {c )-}
{txt} 16{com}. {c )-} 
{txt} 17{com}. {c )-}
{txt} 18{com}. {c )-}
{txt}
{com}. 
. local outcome cog rses cpcs
{txt}
{com}. local hetero1 cogU cogL
{txt}
{com}. local hetero2 cpcsU cpcsL
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. foreach h1 in `hetero1'{c -(}
{txt}  3{com}. foreach h2 in `hetero2'{c -(}
{txt}  4{com}.                 if `dep'_`h1'_`h2'_pv[1,1]<=0.01 {c -(}
{txt}  5{com}.                         local star_`dep'_`h1'_`h2' %3s "***"
{txt}  6{com}.                 {c )-}
{txt}  7{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.01) & (`dep'_`h1'_`h2'_pv[1,1]<=0.05) {c -(}
{txt}  8{com}.                         local star_`dep'_`h1'_`h2' %2s "**"
{txt}  9{com}.                 {c )-}
{txt} 10{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.05) & (`dep'_`h1'_`h2'_pv[1,1]<=0.10) {c -(}
{txt} 11{com}.                         local star_`dep'_`h1'_`h2' %1s "*"
{txt} 12{com}.                 {c )-}
{txt} 13{com}.                 else {c -(}
{txt} 14{com}.                         local star_`dep'_`h1'_`h2'  ""
{txt} 15{com}.                 {c )-}
{txt} 16{com}. {c )-} 
{txt} 17{com}. {c )-}
{txt} 18{com}. {c )-}
{txt}
{com}. 
. /// Table
> 
. tempname hh2
{txt}
{com}. file open `hh2' using "$path_output/hetero_2by2_CPCS.tex", write replace
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "% Author: Kazuma Takakura" _newline
{txt}
{com}. file write `hh2' "% Date: `c(current_date)'" _newline
{txt}
{com}. file write `hh2' "% Time: `c(current_time)'" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. 
. file write `hh2' "\begin{c -(}table{c )-}[h!]\footnotesize" _newline
{txt}
{com}. file write `hh2' "  \centering" _newline
{txt}
{com}. file write `hh2' "  \caption{c -(}Heterogeneity among Baseline Abilites (Math and CPCS){c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:hetero_cpcs{c )-}" _newline
{txt}
{com}. file write `hh2' "\scalebox{c -(}1{c )-}{c -(}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}threeparttable{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "\begin{c -(}tabular{c )-}{c -(}lccccccc{c )-}\toprule" _newline
{txt}
{com}. 
. file write `hh2' " Dependent Variable & \multicolumn{c -(}2{c )-}{c -(}c{c )-}{c -(}Baseline^{c -(}b{c )-}{c )-} & Difference & Obs & N of clusters  \\\midrule\midrule" _newline
{txt}
{com}. file write `hh2' " Rapid math test score^{c -(}a{c )-} & Math Top 50\%  & CPCS Top 50\% & " %04.3f (cog_cogU_rsesU_mean[1,1]) `star_cog_cogU_cpcsU' " & " %02.0f ( n_cog_u_cpcs_u ) " & " %02.0f ( n_clust_cog_u_cpcs_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogU_cpcsU_pv[1,1]) " )  & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cog_cogU_cpcsL_mean[1,1]) `star_cog_cogU_cpcsL' " & " %02.0f ( n_cog_u_cpcs_l ) " & " %02.0f ( n_clust_cog_u_cpcs_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogU_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & Math Bottom 50\%  & CPCS Top 50\% & " %04.3f (cog_cogL_cpcsU_mean[1,1]) `star_cog_cogL_cpcsU' " & " %02.0f ( n_cog_l_cpcs_u ) " & " %02.0f ( n_clust_cog_l_cpcs_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogL_cpcsU_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cog_cogL_cpcsL_mean[1,1]) `star_cog_cogL_cpcsL' " & " %02.0f ( n_cog_l_cpcs_l ) " & " %02.0f ( n_clust_cog_l_cpcs_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogL_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. 
. file write `hh2' " CPCS score^{c -(}a{c )-} & Math Top 50\%  & CPCS Top 50\% & " %04.3f (cpcs_cogU_cpcsU_mean[1,1]) `star_cpcs_cogU_cpcsU' " & " %02.0f ( n_cpcs_u_cog_u ) " & " %02.0f ( n_clust_cpcs_u_cog_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cpcs_cogU_cpcsU_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cpcs_cogU_cpcsL_mean[1,1]) `star_cpcs_cogU_cpcsL' " & " %02.0f ( n_cpcs_l_cog_u ) " & " %02.0f ( n_clust_cpcs_l_cog_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &       & & ( " %04.3f (cpcs_cogU_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & Math Bottom 50\%  & CPCS Top 50\% & " %04.3f (cpcs_cogL_cpcsU_mean[1,1]) `star_cpcs_cogL_cpcsU' " & " %02.0f ( n_cpcs_u_cog_l ) " & " %02.0f ( n_clust_cpcs_u_cog_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cpcs_cogL_cpcsU_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cpcs_cogL_cpcsL_mean[1,1]) `star_cpcs_cogL_cpcsL' " & " %02.0f ( n_cpcs_l_cog_l ) " & " %02.0f ( n_clust_cpcs_l_cog_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &       & & ( " %04.3f (cpcs_cogL_cpcsL_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. 
. file write `hh2' "\\\midrule" _newline
{txt}
{com}. file write `hh2' "\end{c -(}tabular{c )-}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\item (a) Dependent variables are standardized using the average and variance of the whole follow-up sample in the February 2022 survey. " _newline
{txt}
{com}. file write `hh2' "\item (b) Cutoffs are created based on whether their ability to perform each item at the baseline was higher or lower than the median. " _newline
{txt}
{com}. file write `hh2' "\item (c) Wild cluster bootstrap p-values are reported within parentheses. Clusters are schools at the baseline." _newline
{txt}
{com}. file write `hh2' "\item (d) $^*$ Significant at 10\% level; $^{c -(}**{c )-}$ significant at 5\% level; $^{c -(}***{c )-}$ significant at 1\% level. " _newline
{txt}
{com}. file write `hh2' "\end{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}threeparttable{c )-}" _newline
{txt}
{com}. file write `hh2' "{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:addlabel{c )-}%" _newline
{txt}
{com}. file write `hh2' "\end{c -(}table{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. file close `hh2'
{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/mb/s9qljd594gbfyhl2dqnnwlcw0000gn/T//SD56186.000000"
{txt}
{com}. * This is the do file to create "Table 5. Heterogeneity by Baseline Abilites (Math and CPCS)"
. set seed 123123
{txt}
{com}. 
. use "$path_data/temp/followup_student_baseline_score_missing_dummy", replace
{txt}
{com}. 
. egen DT_score_pre_std_med = median(DT_score_pre_std)
{txt}
{com}. gen DT_score_pre_std_upper50 = 1 if DT_score_pre_std>DT_score_pre_std_med
{txt}(121 missing values generated)

{com}. recode DT_score_pre_std_upper50 (.=0)
{txt}(121 changes made to {bf:DT_score_pre_std_upper50})

{com}. 
. egen rosen_pre_std_med = median(rosen_pre_std)
{txt}
{com}. gen rosen_pre_std_upper50 = 1 if rosen_pre_std>rosen_pre_std_med
{txt}(128 missing values generated)

{com}. recode rosen_pre_std_upper50 (.=0)
{txt}(128 changes made to {bf:rosen_pre_std_upper50})

{com}. rename rosen_pre_std_upper50 RSES_std_upper50
{res}{txt}
{com}. 
. egen cpcs_pre_std_med = median(cpcs_pre_std)
{txt}
{com}. gen cpcs_pre_std_upper50 = 1 if cpcs_pre_std>cpcs_pre_std_med
{txt}(135 missing values generated)

{com}. recode cpcs_pre_std_upper50 (.=0)
{txt}(135 changes made to {bf:cpcs_pre_std_upper50})

{com}. rename cpcs_pre_std_upper50 CPCS_std_upper50
{res}{txt}
{com}. 
. 
. /// Cognitive
> wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 59
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 22
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2397567{col 38}{space 1}  -0.86{col 46}{space 3}0.406{col 54}{space 3}-.8075899{col 66}{space 3} .3756059
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_rses_u = e(N)
{txt}
{com}. scalar n_clust_cog_u_rses_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.3459862{col 38}{space 1}  -1.14{col 46}{space 3}0.270{col 54}{space 3}-.9748271{col 66}{space 3} .3033074
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_rses_l = e(N)
{txt}
{com}. scalar n_clust_cog_u_rses_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2419353{col 38}{space 1}  -0.87{col 46}{space 3}0.416{col 54}{space 3}-.8268344{col 66}{space 3} .4462363
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_rses_u = e(N)
{txt}
{com}. scalar n_clust_cog_l_rses_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0781293{col 38}{space 1}  -0.29{col 46}{space 3}0.854{col 54}{space 3}-.6103821{col 66}{space 3} .5647216
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_rses_l = e(N)
{txt}
{com}. scalar n_clust_cog_l_rses_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 55
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0533319{col 38}{space 1}   0.17{col 46}{space 3}0.834{col 54}{space 3}-.5578962{col 66}{space 3} .7962924
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_cpcs_u = e(N)
{txt}
{com}. scalar n_clust_cog_u_cpcs_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 67
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.5660393{col 38}{space 1}  -1.84{col 46}{space 3}0.076{col 54}{space 3} -1.18724{col 66}{space 3} .1020071
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_cpcs_l = e(N)
{txt}
{com}. scalar n_clust_cog_u_cpcs_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0295186{col 38}{space 1}   0.10{col 46}{space 3}0.946{col 54}{space 3}-.5946687{col 66}{space 3} .7073626
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_cpcs_u = e(N)
{txt}
{com}. scalar n_clust_cog_l_cpcs_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 68
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  -.19243{col 38}{space 1}  -0.73{col 46}{space 3}0.494{col 54}{space 3}-.7208236{col 66}{space 3} .3922755
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_cpcs_l = e(N)
{txt}
{com}. scalar n_clust_cog_l_cpcs_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. 
. 
. /// RSES
> wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:29})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:19})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .9579135{col 38}{space 1}   3.36{col 46}{space 3}0.008{col 54}{space 3} .3325656{col 66}{space 3} 1.573549
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_u_cog_u = e(N)
{txt}
{com}. scalar n_clust_rses_u_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 61
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0615952{col 38}{space 1}   0.32{col 46}{space 3}0.794{col 54}{space 3} -.364723{col 66}{space 3} .4438736
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_l_cog_u = e(N)
{txt}
{com}. scalar n_clust_rses_l_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .4920182{col 38}{space 1}   1.44{col 46}{space 3}0.188{col 54}{space 3}-.2899132{col 66}{space 3} 1.184523
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_u_cog_l = e(N)
{txt}
{com}. scalar n_clust_rses_u_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1388312{col 38}{space 1}   0.42{col 46}{space 3}0.710{col 54}{space 3}-.5966777{col 66}{space 3} .8491857
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_l_cog_l = e(N)
{txt}
{com}. scalar n_clust_rses_l_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 52
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 20
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .6664341{col 38}{space 1}   3.26{col 46}{space 3}0.004{col 54}{space 3}  .253571{col 66}{space 3} 1.109131
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3847293{col 38}{space 1}   1.53{col 46}{space 3}0.156{col 54}{space 3}-.1792371{col 66}{space 3}   .99175
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2062219{col 38}{space 1}   0.47{col 46}{space 3}0.664{col 54}{space 3}-.8651163{col 66}{space 3} 1.165542
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 66
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3332931{col 38}{space 1}   0.96{col 46}{space 3}0.400{col 54}{space 3}-.5746995{col 66}{space 3} 1.115284
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. 
. 
. /// CPCS
> 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} 1.020132{col 38}{space 1}   3.66{col 46}{space 3}0.004{col 54}{space 3} .3478905{col 66}{space 3} 1.719569
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 61
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0665639{col 38}{space 1}   0.34{col 46}{space 3}0.700{col 54}{space 3}-.3415325{col 66}{space 3} .4978466
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}30{text} done{text} ({result:30})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .5458399{col 38}{space 1}   1.63{col 46}{space 3}0.118{col 54}{space 3}-.2237054{col 66}{space 3}   1.2581
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1431476{col 38}{space 1}   0.41{col 46}{space 3}0.660{col 54}{space 3}-.6874383{col 66}{space 3} 1.007023
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:29})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 52
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 20
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  .774167{col 38}{space 1}   3.68{col 46}{space 3}0.004{col 54}{space 3} .3512991{col 66}{space 3} 1.242434
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_u_cog_u = e(N)
{txt}
{com}. scalar n_clust_cpcs_u_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3575321{col 38}{space 1}   1.51{col 46}{space 3}0.170{col 54}{space 3}-.1472086{col 66}{space 3} .8699089
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_l_cog_u = e(N)
{txt}
{com}. scalar n_clust_cpcs_l_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2792052{col 38}{space 1}   0.63{col 46}{space 3}0.580{col 54}{space 3}-.8448347{col 66}{space 3}  1.30284
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_u_cog_l = e(N)
{txt}
{com}. scalar n_clust_cpcs_u_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 66
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}   .33148{col 38}{space 1}   0.95{col 46}{space 3}0.432{col 54}{space 3}-.4980161{col 66}{space 3} 1.116675
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_l_cog_l = e(N)
{txt}
{com}. scalar n_clust_cpcs_l_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. // significant level
. 
. 
. local outcome cog rses cpcs
{txt}
{com}. local hetero1 cogU cogL
{txt}
{com}. local hetero2 rsesU rsesL
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. foreach h1 in `hetero1'{c -(}
{txt}  3{com}. foreach h2 in `hetero2'{c -(}
{txt}  4{com}.                 if `dep'_`h1'_`h2'_pv[1,1]<=0.01 {c -(}
{txt}  5{com}.                         local star_`dep'_`h1'_`h2' %3s "***"
{txt}  6{com}.                 {c )-}
{txt}  7{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.01) & (`dep'_`h1'_`h2'_pv[1,1]<=0.05) {c -(}
{txt}  8{com}.                         local star_`dep'_`h1'_`h2' %2s "**"
{txt}  9{com}.                 {c )-}
{txt} 10{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.05) & (`dep'_`h1'_`h2'_pv[1,1]<=0.10) {c -(}
{txt} 11{com}.                         local star_`dep'_`h1'_`h2' %1s "*"
{txt} 12{com}.                 {c )-}
{txt} 13{com}.                 else {c -(}
{txt} 14{com}.                         local star_`dep'_`h1'_`h2'  ""
{txt} 15{com}.                 {c )-}
{txt} 16{com}. {c )-} 
{txt} 17{com}. {c )-}
{txt} 18{com}. {c )-}
{txt}
{com}. 
. local outcome cog rses cpcs
{txt}
{com}. local hetero1 cogU cogL
{txt}
{com}. local hetero2 cpcsU cpcsL
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. foreach h1 in `hetero1'{c -(}
{txt}  3{com}. foreach h2 in `hetero2'{c -(}
{txt}  4{com}.                 if `dep'_`h1'_`h2'_pv[1,1]<=0.01 {c -(}
{txt}  5{com}.                         local star_`dep'_`h1'_`h2' %3s "***"
{txt}  6{com}.                 {c )-}
{txt}  7{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.01) & (`dep'_`h1'_`h2'_pv[1,1]<=0.05) {c -(}
{txt}  8{com}.                         local star_`dep'_`h1'_`h2' %2s "**"
{txt}  9{com}.                 {c )-}
{txt} 10{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.05) & (`dep'_`h1'_`h2'_pv[1,1]<=0.10) {c -(}
{txt} 11{com}.                         local star_`dep'_`h1'_`h2' %1s "*"
{txt} 12{com}.                 {c )-}
{txt} 13{com}.                 else {c -(}
{txt} 14{com}.                         local star_`dep'_`h1'_`h2'  ""
{txt} 15{com}.                 {c )-}
{txt} 16{com}. {c )-} 
{txt} 17{com}. {c )-}
{txt} 18{com}. {c )-}
{txt}
{com}. 
. /// Table
> 
. tempname hh2
{txt}
{com}. file open `hh2' using "$path_output/hetero_2by2_CPCS.tex", write replace
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "% Author: Kazuma Takakura" _newline
{txt}
{com}. file write `hh2' "% Date: `c(current_date)'" _newline
{txt}
{com}. file write `hh2' "% Time: `c(current_time)'" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. 
. file write `hh2' "\begin{c -(}table{c )-}[h!]\footnotesize" _newline
{txt}
{com}. file write `hh2' "  \centering" _newline
{txt}
{com}. file write `hh2' "  \caption{c -(}Heterogeneity among Baseline Abilites (Math and CPCS){c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:hetero_cpcs{c )-}" _newline
{txt}
{com}. file write `hh2' "\scalebox{c -(}1{c )-}{c -(}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}threeparttable{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "\begin{c -(}tabular{c )-}{c -(}lccccccc{c )-}\toprule" _newline
{txt}
{com}. 
. file write `hh2' " Dependent Variable & \multicolumn{c -(}2{c )-}{c -(}c{c )-}{c -(}Baseline^{c -(}b{c )-}{c )-} & Difference & Obs & N of clusters  \\\midrule\midrule" _newline
{txt}
{com}. file write `hh2' " Rapid math test score^{c -(}a{c )-} & Math Top 50\%  & CPCS Top 50\% & " %04.3f (cog_cogU_rsesU_mean[1,1]) `star_cog_cogU_cpcsU' " & " %02.0f ( n_cog_u_cpcs_u ) " & " %02.0f ( n_clust_cog_u_cpcs_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogU_cpcsU_pv[1,1]) " )  & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cog_cogU_cpcsL_mean[1,1]) `star_cog_cogU_cpcsL' " & " %02.0f ( n_cog_u_cpcs_l ) " & " %02.0f ( n_clust_cog_u_cpcs_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogU_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & Math Bottom 50\%  & CPCS Top 50\% & " %04.3f (cog_cogL_cpcsU_mean[1,1]) `star_cog_cogL_cpcsU' " & " %02.0f ( n_cog_l_cpcs_u ) " & " %02.0f ( n_clust_cog_l_cpcs_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogL_cpcsU_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cog_cogL_cpcsL_mean[1,1]) `star_cog_cogL_cpcsL' " & " %02.0f ( n_cog_l_cpcs_l ) " & " %02.0f ( n_clust_cog_l_cpcs_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogL_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. 
. file write `hh2' " CPCS score^{c -(}a{c )-} & Math Top 50\%  & CPCS Top 50\% & " %04.3f (cpcs_cogU_cpcsU_mean[1,1]) `star_cpcs_cogU_cpcsU' " & " %02.0f ( n_cpcs_u_cog_u ) " & " %02.0f ( n_clust_cpcs_u_cog_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cpcs_cogU_cpcsU_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cpcs_cogU_cpcsL_mean[1,1]) `star_cpcs_cogU_cpcsL' " & " %02.0f ( n_cpcs_l_cog_u ) " & " %02.0f ( n_clust_cpcs_l_cog_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &       & & ( " %04.3f (cpcs_cogU_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & Math Bottom 50\%  & CPCS Top 50\% & " %04.3f (cpcs_cogL_cpcsU_mean[1,1]) `star_cpcs_cogL_cpcsU' " & " %02.0f ( n_cpcs_u_cog_l ) " & " %02.0f ( n_clust_cpcs_u_cog_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cpcs_cogL_cpcsU_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cpcs_cogL_cpcsL_mean[1,1]) `star_cpcs_cogL_cpcsL' " & " %02.0f ( n_cpcs_l_cog_l ) " & " %02.0f ( n_clust_cpcs_l_cog_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &       & & ( " %04.3f (cpcs_cogL_cpcsL_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. 
. file write `hh2' "\\\midrule" _newline
{txt}
{com}. file write `hh2' "\end{c -(}tabular{c )-}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\item (a) Dependent variables are standardized using the average and variance of the whole follow-up sample in the February 2022 survey. " _newline
{txt}
{com}. file write `hh2' "\item (b) Cutoffs are created based on whether their ability to perform each item at the baseline was higher or lower than the median. " _newline
{txt}
{com}. file write `hh2' "\item (c) Wild cluster bootstrap p-values are reported within parentheses. Clusters are schools at the baseline." _newline
{txt}
{com}. file write `hh2' "\item (d) $^*$ Significant at 10\% level; $^{c -(}**{c )-}$ significant at 5\% level; $^{c -(}***{c )-}$ significant at 1\% level. " _newline
{txt}
{com}. file write `hh2' "\end{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}threeparttable{c )-}" _newline
{txt}
{com}. file write `hh2' "{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:addlabel{c )-}%" _newline
{txt}
{com}. file write `hh2' "\end{c -(}table{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. file close `hh2'
{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/mb/s9qljd594gbfyhl2dqnnwlcw0000gn/T//SD56186.000000"
{txt}
{com}. * This is the do file to create "Table 5. Heterogeneity by Baseline Abilites (Math and CPCS)"
. set seed 1231231
{txt}
{com}. 
. use "$path_data/temp/followup_student_baseline_score_missing_dummy", replace
{txt}
{com}. 
. egen DT_score_pre_std_med = median(DT_score_pre_std)
{txt}
{com}. gen DT_score_pre_std_upper50 = 1 if DT_score_pre_std>DT_score_pre_std_med
{txt}(121 missing values generated)

{com}. recode DT_score_pre_std_upper50 (.=0)
{txt}(121 changes made to {bf:DT_score_pre_std_upper50})

{com}. 
. egen rosen_pre_std_med = median(rosen_pre_std)
{txt}
{com}. gen rosen_pre_std_upper50 = 1 if rosen_pre_std>rosen_pre_std_med
{txt}(128 missing values generated)

{com}. recode rosen_pre_std_upper50 (.=0)
{txt}(128 changes made to {bf:rosen_pre_std_upper50})

{com}. rename rosen_pre_std_upper50 RSES_std_upper50
{res}{txt}
{com}. 
. egen cpcs_pre_std_med = median(cpcs_pre_std)
{txt}
{com}. gen cpcs_pre_std_upper50 = 1 if cpcs_pre_std>cpcs_pre_std_med
{txt}(135 missing values generated)

{com}. recode cpcs_pre_std_upper50 (.=0)
{txt}(135 changes made to {bf:cpcs_pre_std_upper50})

{com}. rename cpcs_pre_std_upper50 CPCS_std_upper50
{res}{txt}
{com}. 
. 
. /// Cognitive
> wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 59
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 22
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2397567{col 38}{space 1}  -0.86{col 46}{space 3}0.426{col 54}{space 3}-.8209718{col 66}{space 3} .3842474
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_rses_u = e(N)
{txt}
{com}. scalar n_clust_cog_u_rses_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.3459862{col 38}{space 1}  -1.14{col 46}{space 3}0.276{col 54}{space 3}-1.007236{col 66}{space 3} .3184601
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_rses_l = e(N)
{txt}
{com}. scalar n_clust_cog_u_rses_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2419353{col 38}{space 1}  -0.87{col 46}{space 3}0.372{col 54}{space 3}-.7920432{col 66}{space 3} .3464601
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_rses_u = e(N)
{txt}
{com}. scalar n_clust_cog_l_rses_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0781293{col 38}{space 1}  -0.29{col 46}{space 3}0.722{col 54}{space 3}-.5797204{col 66}{space 3} .5344312
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_rses_l = e(N)
{txt}
{com}. scalar n_clust_cog_l_rses_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 55
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0533319{col 38}{space 1}   0.17{col 46}{space 3}0.822{col 54}{space 3}-.5833549{col 66}{space 3} .7280251
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_cpcs_u = e(N)
{txt}
{com}. scalar n_clust_cog_u_cpcs_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:29})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 67
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.5660393{col 38}{space 1}  -1.84{col 46}{space 3}0.078{col 54}{space 3}-1.246116{col 66}{space 3} .0666146
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_cpcs_l = e(N)
{txt}
{com}. scalar n_clust_cog_u_cpcs_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0295186{col 38}{space 1}   0.10{col 46}{space 3}0.936{col 54}{space 3}-.5949447{col 66}{space 3} .7707663
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_cpcs_u = e(N)
{txt}
{com}. scalar n_clust_cog_l_cpcs_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 68
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  -.19243{col 38}{space 1}  -0.73{col 46}{space 3}0.458{col 54}{space 3}-.7459849{col 66}{space 3} .3809649
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_cpcs_l = e(N)
{txt}
{com}. scalar n_clust_cog_l_cpcs_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. 
. 
. /// RSES
> wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .9579135{col 38}{space 1}   3.36{col 46}{space 3}0.010{col 54}{space 3} .2847872{col 66}{space 3} 1.619408
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_u_cog_u = e(N)
{txt}
{com}. scalar n_clust_rses_u_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 61
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0615952{col 38}{space 1}   0.32{col 46}{space 3}0.768{col 54}{space 3}-.3317923{col 66}{space 3} .4552424
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_l_cog_u = e(N)
{txt}
{com}. scalar n_clust_rses_l_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .4920182{col 38}{space 1}   1.44{col 46}{space 3}0.204{col 54}{space 3}-.2764866{col 66}{space 3}  1.16417
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_u_cog_l = e(N)
{txt}
{com}. scalar n_clust_rses_u_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1388312{col 38}{space 1}   0.42{col 46}{space 3}0.670{col 54}{space 3}-.5589822{col 66}{space 3} .9059031
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_l_cog_l = e(N)
{txt}
{com}. scalar n_clust_rses_l_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 52
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 20
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .6664341{col 38}{space 1}   3.26{col 46}{space 3}0.010{col 54}{space 3} .2686253{col 66}{space 3} 1.129464
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3847293{col 38}{space 1}   1.53{col 46}{space 3}0.134{col 54}{space 3}-.1377196{col 66}{space 3} .9472449
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2062219{col 38}{space 1}   0.47{col 46}{space 3}0.650{col 54}{space 3}-.9157329{col 66}{space 3} 1.156985
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 66
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3332931{col 38}{space 1}   0.96{col 46}{space 3}0.402{col 54}{space 3}-.5096596{col 66}{space 3} 1.080435
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. 
. 
. /// CPCS
> 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} 1.020132{col 38}{space 1}   3.66{col 46}{space 3}0.004{col 54}{space 3} .3536923{col 66}{space 3} 1.687578
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 61
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0665639{col 38}{space 1}   0.34{col 46}{space 3}0.764{col 54}{space 3}-.3841886{col 66}{space 3} .4862318
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .5458399{col 38}{space 1}   1.63{col 46}{space 3}0.176{col 54}{space 3}-.3569615{col 66}{space 3} 1.325397
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1431476{col 38}{space 1}   0.41{col 46}{space 3}0.720{col 54}{space 3}-.6740223{col 66}{space 3} .9922436
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 52
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 20
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  .774167{col 38}{space 1}   3.68{col 46}{space 3}0.004{col 54}{space 3} .3384549{col 66}{space 3} 1.278442
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_u_cog_u = e(N)
{txt}
{com}. scalar n_clust_cpcs_u_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3575321{col 38}{space 1}   1.51{col 46}{space 3}0.150{col 54}{space 3}-.1421439{col 66}{space 3} .9113857
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_l_cog_u = e(N)
{txt}
{com}. scalar n_clust_cpcs_l_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2792052{col 38}{space 1}   0.63{col 46}{space 3}0.546{col 54}{space 3}-.9876253{col 66}{space 3} 1.272913
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_u_cog_l = e(N)
{txt}
{com}. scalar n_clust_cpcs_u_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 66
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}   .33148{col 38}{space 1}   0.95{col 46}{space 3}0.378{col 54}{space 3}-.5180712{col 66}{space 3} 1.130238
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_l_cog_l = e(N)
{txt}
{com}. scalar n_clust_cpcs_l_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. // significant level
. 
. 
. local outcome cog rses cpcs
{txt}
{com}. local hetero1 cogU cogL
{txt}
{com}. local hetero2 rsesU rsesL
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. foreach h1 in `hetero1'{c -(}
{txt}  3{com}. foreach h2 in `hetero2'{c -(}
{txt}  4{com}.                 if `dep'_`h1'_`h2'_pv[1,1]<=0.01 {c -(}
{txt}  5{com}.                         local star_`dep'_`h1'_`h2' %3s "***"
{txt}  6{com}.                 {c )-}
{txt}  7{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.01) & (`dep'_`h1'_`h2'_pv[1,1]<=0.05) {c -(}
{txt}  8{com}.                         local star_`dep'_`h1'_`h2' %2s "**"
{txt}  9{com}.                 {c )-}
{txt} 10{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.05) & (`dep'_`h1'_`h2'_pv[1,1]<=0.10) {c -(}
{txt} 11{com}.                         local star_`dep'_`h1'_`h2' %1s "*"
{txt} 12{com}.                 {c )-}
{txt} 13{com}.                 else {c -(}
{txt} 14{com}.                         local star_`dep'_`h1'_`h2'  ""
{txt} 15{com}.                 {c )-}
{txt} 16{com}. {c )-} 
{txt} 17{com}. {c )-}
{txt} 18{com}. {c )-}
{txt}
{com}. 
. local outcome cog rses cpcs
{txt}
{com}. local hetero1 cogU cogL
{txt}
{com}. local hetero2 cpcsU cpcsL
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. foreach h1 in `hetero1'{c -(}
{txt}  3{com}. foreach h2 in `hetero2'{c -(}
{txt}  4{com}.                 if `dep'_`h1'_`h2'_pv[1,1]<=0.01 {c -(}
{txt}  5{com}.                         local star_`dep'_`h1'_`h2' %3s "***"
{txt}  6{com}.                 {c )-}
{txt}  7{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.01) & (`dep'_`h1'_`h2'_pv[1,1]<=0.05) {c -(}
{txt}  8{com}.                         local star_`dep'_`h1'_`h2' %2s "**"
{txt}  9{com}.                 {c )-}
{txt} 10{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.05) & (`dep'_`h1'_`h2'_pv[1,1]<=0.10) {c -(}
{txt} 11{com}.                         local star_`dep'_`h1'_`h2' %1s "*"
{txt} 12{com}.                 {c )-}
{txt} 13{com}.                 else {c -(}
{txt} 14{com}.                         local star_`dep'_`h1'_`h2'  ""
{txt} 15{com}.                 {c )-}
{txt} 16{com}. {c )-} 
{txt} 17{com}. {c )-}
{txt} 18{com}. {c )-}
{txt}
{com}. 
. /// Table
> 
. tempname hh2
{txt}
{com}. file open `hh2' using "$path_output/hetero_2by2_CPCS.tex", write replace
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "% Author: Kazuma Takakura" _newline
{txt}
{com}. file write `hh2' "% Date: `c(current_date)'" _newline
{txt}
{com}. file write `hh2' "% Time: `c(current_time)'" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. 
. file write `hh2' "\begin{c -(}table{c )-}[h!]\footnotesize" _newline
{txt}
{com}. file write `hh2' "  \centering" _newline
{txt}
{com}. file write `hh2' "  \caption{c -(}Heterogeneity among Baseline Abilites (Math and CPCS){c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:hetero_cpcs{c )-}" _newline
{txt}
{com}. file write `hh2' "\scalebox{c -(}1{c )-}{c -(}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}threeparttable{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "\begin{c -(}tabular{c )-}{c -(}lccccccc{c )-}\toprule" _newline
{txt}
{com}. 
. file write `hh2' " Dependent Variable & \multicolumn{c -(}2{c )-}{c -(}c{c )-}{c -(}Baseline^{c -(}b{c )-}{c )-} & Difference & Obs & N of clusters  \\\midrule\midrule" _newline
{txt}
{com}. file write `hh2' " Rapid math test score^{c -(}a{c )-} & Math Top 50\%  & CPCS Top 50\% & " %04.3f (cog_cogU_rsesU_mean[1,1]) `star_cog_cogU_cpcsU' " & " %02.0f ( n_cog_u_cpcs_u ) " & " %02.0f ( n_clust_cog_u_cpcs_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogU_cpcsU_pv[1,1]) " )  & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cog_cogU_cpcsL_mean[1,1]) `star_cog_cogU_cpcsL' " & " %02.0f ( n_cog_u_cpcs_l ) " & " %02.0f ( n_clust_cog_u_cpcs_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogU_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & Math Bottom 50\%  & CPCS Top 50\% & " %04.3f (cog_cogL_cpcsU_mean[1,1]) `star_cog_cogL_cpcsU' " & " %02.0f ( n_cog_l_cpcs_u ) " & " %02.0f ( n_clust_cog_l_cpcs_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogL_cpcsU_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cog_cogL_cpcsL_mean[1,1]) `star_cog_cogL_cpcsL' " & " %02.0f ( n_cog_l_cpcs_l ) " & " %02.0f ( n_clust_cog_l_cpcs_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogL_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. 
. file write `hh2' " CPCS score^{c -(}a{c )-} & Math Top 50\%  & CPCS Top 50\% & " %04.3f (cpcs_cogU_cpcsU_mean[1,1]) `star_cpcs_cogU_cpcsU' " & " %02.0f ( n_cpcs_u_cog_u ) " & " %02.0f ( n_clust_cpcs_u_cog_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cpcs_cogU_cpcsU_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cpcs_cogU_cpcsL_mean[1,1]) `star_cpcs_cogU_cpcsL' " & " %02.0f ( n_cpcs_l_cog_u ) " & " %02.0f ( n_clust_cpcs_l_cog_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &       & & ( " %04.3f (cpcs_cogU_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & Math Bottom 50\%  & CPCS Top 50\% & " %04.3f (cpcs_cogL_cpcsU_mean[1,1]) `star_cpcs_cogL_cpcsU' " & " %02.0f ( n_cpcs_u_cog_l ) " & " %02.0f ( n_clust_cpcs_u_cog_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cpcs_cogL_cpcsU_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cpcs_cogL_cpcsL_mean[1,1]) `star_cpcs_cogL_cpcsL' " & " %02.0f ( n_cpcs_l_cog_l ) " & " %02.0f ( n_clust_cpcs_l_cog_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &       & & ( " %04.3f (cpcs_cogL_cpcsL_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. 
. file write `hh2' "\\\midrule" _newline
{txt}
{com}. file write `hh2' "\end{c -(}tabular{c )-}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\item (a) Dependent variables are standardized using the average and variance of the whole follow-up sample in the February 2022 survey. " _newline
{txt}
{com}. file write `hh2' "\item (b) Cutoffs are created based on whether their ability to perform each item at the baseline was higher or lower than the median. " _newline
{txt}
{com}. file write `hh2' "\item (c) Wild cluster bootstrap p-values are reported within parentheses. Clusters are schools at the baseline." _newline
{txt}
{com}. file write `hh2' "\item (d) $^*$ Significant at 10\% level; $^{c -(}**{c )-}$ significant at 5\% level; $^{c -(}***{c )-}$ significant at 1\% level. " _newline
{txt}
{com}. file write `hh2' "\end{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}threeparttable{c )-}" _newline
{txt}
{com}. file write `hh2' "{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:addlabel{c )-}%" _newline
{txt}
{com}. file write `hh2' "\end{c -(}table{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. file close `hh2'
{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/mb/s9qljd594gbfyhl2dqnnwlcw0000gn/T//SD56186.000000"
{txt}
{com}. * This is the do file to create "Table 5. Heterogeneity by Baseline Abilites (Math and CPCS)"
. set seed 123456789
{txt}
{com}. 
. use "$path_data/temp/followup_student_baseline_score_missing_dummy", replace
{txt}
{com}. 
. egen DT_score_pre_std_med = median(DT_score_pre_std)
{txt}
{com}. gen DT_score_pre_std_upper50 = 1 if DT_score_pre_std>DT_score_pre_std_med
{txt}(121 missing values generated)

{com}. recode DT_score_pre_std_upper50 (.=0)
{txt}(121 changes made to {bf:DT_score_pre_std_upper50})

{com}. 
. egen rosen_pre_std_med = median(rosen_pre_std)
{txt}
{com}. gen rosen_pre_std_upper50 = 1 if rosen_pre_std>rosen_pre_std_med
{txt}(128 missing values generated)

{com}. recode rosen_pre_std_upper50 (.=0)
{txt}(128 changes made to {bf:rosen_pre_std_upper50})

{com}. rename rosen_pre_std_upper50 RSES_std_upper50
{res}{txt}
{com}. 
. egen cpcs_pre_std_med = median(cpcs_pre_std)
{txt}
{com}. gen cpcs_pre_std_upper50 = 1 if cpcs_pre_std>cpcs_pre_std_med
{txt}(135 missing values generated)

{com}. recode cpcs_pre_std_upper50 (.=0)
{txt}(135 changes made to {bf:cpcs_pre_std_upper50})

{com}. rename cpcs_pre_std_upper50 CPCS_std_upper50
{res}{txt}
{com}. 
. 
. /// Cognitive
> wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 59
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 22
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2397567{col 38}{space 1}  -0.86{col 46}{space 3}0.444{col 54}{space 3}-.8482181{col 66}{space 3} .4245563
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_rses_u = e(N)
{txt}
{com}. scalar n_clust_cog_u_rses_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.3459862{col 38}{space 1}  -1.14{col 46}{space 3}0.308{col 54}{space 3}-.9544591{col 66}{space 3} .3093175
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_rses_l = e(N)
{txt}
{com}. scalar n_clust_cog_u_rses_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2419353{col 38}{space 1}  -0.87{col 46}{space 3}0.396{col 54}{space 3}-.7972792{col 66}{space 3} .4588476
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_rses_u = e(N)
{txt}
{com}. scalar n_clust_cog_l_rses_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0781293{col 38}{space 1}  -0.29{col 46}{space 3}0.796{col 54}{space 3}-.6078019{col 66}{space 3} .5276374
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_rses_l = e(N)
{txt}
{com}. scalar n_clust_cog_l_rses_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 55
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0533319{col 38}{space 1}   0.17{col 46}{space 3}0.880{col 54}{space 3}-.6389376{col 66}{space 3} .7984889
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_cpcs_u = e(N)
{txt}
{com}. scalar n_clust_cog_u_cpcs_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:29})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 67
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.5660393{col 38}{space 1}  -1.84{col 46}{space 3}0.080{col 54}{space 3}-1.211552{col 66}{space 3}  .088021
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_cpcs_l = e(N)
{txt}
{com}. scalar n_clust_cog_u_cpcs_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0295186{col 38}{space 1}   0.10{col 46}{space 3}0.934{col 54}{space 3}-.5765139{col 66}{space 3} .7094547
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_cpcs_u = e(N)
{txt}
{com}. scalar n_clust_cog_l_cpcs_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 68
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  -.19243{col 38}{space 1}  -0.73{col 46}{space 3}0.476{col 54}{space 3} -.723504{col 66}{space 3} .4187932
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_cpcs_l = e(N)
{txt}
{com}. scalar n_clust_cog_l_cpcs_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. 
. 
. /// RSES
> wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .9579135{col 38}{space 1}   3.36{col 46}{space 3}0.006{col 54}{space 3} .2689428{col 66}{space 3}  1.68245
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_u_cog_u = e(N)
{txt}
{com}. scalar n_clust_rses_u_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 61
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0615952{col 38}{space 1}   0.32{col 46}{space 3}0.770{col 54}{space 3}-.3348444{col 66}{space 3} .4830784
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_l_cog_u = e(N)
{txt}
{com}. scalar n_clust_rses_l_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .4920182{col 38}{space 1}   1.44{col 46}{space 3}0.202{col 54}{space 3}  -.37065{col 66}{space 3} 1.184859
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_u_cog_l = e(N)
{txt}
{com}. scalar n_clust_rses_u_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1388312{col 38}{space 1}   0.42{col 46}{space 3}0.708{col 54}{space 3}-.5740465{col 66}{space 3} .9344912
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_l_cog_l = e(N)
{txt}
{com}. scalar n_clust_rses_l_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 52
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 20
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .6664341{col 38}{space 1}   3.26{col 46}{space 3}0.006{col 54}{space 3} .2209861{col 66}{space 3} 1.098663
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3847293{col 38}{space 1}   1.53{col 46}{space 3}0.116{col 54}{space 3}-.1451568{col 66}{space 3}  .968164
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2062219{col 38}{space 1}   0.47{col 46}{space 3}0.750{col 54}{space 3}-1.037254{col 66}{space 3} 1.146144
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 66
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3332931{col 38}{space 1}   0.96{col 46}{space 3}0.380{col 54}{space 3}-.5124486{col 66}{space 3}  1.09401
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. 
. 
. /// CPCS
> 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} 1.020132{col 38}{space 1}   3.66{col 46}{space 3}0.012{col 54}{space 3} .3670563{col 66}{space 3} 1.702239
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 61
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0665639{col 38}{space 1}   0.34{col 46}{space 3}0.744{col 54}{space 3}-.3389043{col 66}{space 3} .4553432
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .5458399{col 38}{space 1}   1.63{col 46}{space 3}0.162{col 54}{space 3}-.3122506{col 66}{space 3} 1.269623
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1431476{col 38}{space 1}   0.41{col 46}{space 3}0.736{col 54}{space 3}-.7120483{col 66}{space 3} .9219637
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 52
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 20
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  .774167{col 38}{space 1}   3.68{col 46}{space 3}0.000{col 54}{space 3} .3351949{col 66}{space 3} 1.260108
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_u_cog_u = e(N)
{txt}
{com}. scalar n_clust_cpcs_u_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3575321{col 38}{space 1}   1.51{col 46}{space 3}0.168{col 54}{space 3} -.142855{col 66}{space 3} .8544713
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_l_cog_u = e(N)
{txt}
{com}. scalar n_clust_cpcs_l_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2792052{col 38}{space 1}   0.63{col 46}{space 3}0.578{col 54}{space 3} -.857953{col 66}{space 3} 1.283201
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_u_cog_l = e(N)
{txt}
{com}. scalar n_clust_cpcs_u_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 66
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}   .33148{col 38}{space 1}   0.95{col 46}{space 3}0.378{col 54}{space 3}-.4632287{col 66}{space 3}    1.005
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_l_cog_l = e(N)
{txt}
{com}. scalar n_clust_cpcs_l_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. // significant level
. 
. 
. local outcome cog rses cpcs
{txt}
{com}. local hetero1 cogU cogL
{txt}
{com}. local hetero2 rsesU rsesL
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. foreach h1 in `hetero1'{c -(}
{txt}  3{com}. foreach h2 in `hetero2'{c -(}
{txt}  4{com}.                 if `dep'_`h1'_`h2'_pv[1,1]<=0.01 {c -(}
{txt}  5{com}.                         local star_`dep'_`h1'_`h2' %3s "***"
{txt}  6{com}.                 {c )-}
{txt}  7{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.01) & (`dep'_`h1'_`h2'_pv[1,1]<=0.05) {c -(}
{txt}  8{com}.                         local star_`dep'_`h1'_`h2' %2s "**"
{txt}  9{com}.                 {c )-}
{txt} 10{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.05) & (`dep'_`h1'_`h2'_pv[1,1]<=0.10) {c -(}
{txt} 11{com}.                         local star_`dep'_`h1'_`h2' %1s "*"
{txt} 12{com}.                 {c )-}
{txt} 13{com}.                 else {c -(}
{txt} 14{com}.                         local star_`dep'_`h1'_`h2'  ""
{txt} 15{com}.                 {c )-}
{txt} 16{com}. {c )-} 
{txt} 17{com}. {c )-}
{txt} 18{com}. {c )-}
{txt}
{com}. 
. local outcome cog rses cpcs
{txt}
{com}. local hetero1 cogU cogL
{txt}
{com}. local hetero2 cpcsU cpcsL
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. foreach h1 in `hetero1'{c -(}
{txt}  3{com}. foreach h2 in `hetero2'{c -(}
{txt}  4{com}.                 if `dep'_`h1'_`h2'_pv[1,1]<=0.01 {c -(}
{txt}  5{com}.                         local star_`dep'_`h1'_`h2' %3s "***"
{txt}  6{com}.                 {c )-}
{txt}  7{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.01) & (`dep'_`h1'_`h2'_pv[1,1]<=0.05) {c -(}
{txt}  8{com}.                         local star_`dep'_`h1'_`h2' %2s "**"
{txt}  9{com}.                 {c )-}
{txt} 10{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.05) & (`dep'_`h1'_`h2'_pv[1,1]<=0.10) {c -(}
{txt} 11{com}.                         local star_`dep'_`h1'_`h2' %1s "*"
{txt} 12{com}.                 {c )-}
{txt} 13{com}.                 else {c -(}
{txt} 14{com}.                         local star_`dep'_`h1'_`h2'  ""
{txt} 15{com}.                 {c )-}
{txt} 16{com}. {c )-} 
{txt} 17{com}. {c )-}
{txt} 18{com}. {c )-}
{txt}
{com}. 
. /// Table
> 
. tempname hh2
{txt}
{com}. file open `hh2' using "$path_output/hetero_2by2_CPCS.tex", write replace
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "% Author: Kazuma Takakura" _newline
{txt}
{com}. file write `hh2' "% Date: `c(current_date)'" _newline
{txt}
{com}. file write `hh2' "% Time: `c(current_time)'" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. 
. file write `hh2' "\begin{c -(}table{c )-}[h!]\footnotesize" _newline
{txt}
{com}. file write `hh2' "  \centering" _newline
{txt}
{com}. file write `hh2' "  \caption{c -(}Heterogeneity among Baseline Abilites (Math and CPCS){c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:hetero_cpcs{c )-}" _newline
{txt}
{com}. file write `hh2' "\scalebox{c -(}1{c )-}{c -(}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}threeparttable{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "\begin{c -(}tabular{c )-}{c -(}lccccccc{c )-}\toprule" _newline
{txt}
{com}. 
. file write `hh2' " Dependent Variable & \multicolumn{c -(}2{c )-}{c -(}c{c )-}{c -(}Baseline^{c -(}b{c )-}{c )-} & Difference & Obs & N of clusters  \\\midrule\midrule" _newline
{txt}
{com}. file write `hh2' " Rapid math test score^{c -(}a{c )-} & Math Top 50\%  & CPCS Top 50\% & " %04.3f (cog_cogU_rsesU_mean[1,1]) `star_cog_cogU_cpcsU' " & " %02.0f ( n_cog_u_cpcs_u ) " & " %02.0f ( n_clust_cog_u_cpcs_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogU_cpcsU_pv[1,1]) " )  & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cog_cogU_cpcsL_mean[1,1]) `star_cog_cogU_cpcsL' " & " %02.0f ( n_cog_u_cpcs_l ) " & " %02.0f ( n_clust_cog_u_cpcs_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogU_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & Math Bottom 50\%  & CPCS Top 50\% & " %04.3f (cog_cogL_cpcsU_mean[1,1]) `star_cog_cogL_cpcsU' " & " %02.0f ( n_cog_l_cpcs_u ) " & " %02.0f ( n_clust_cog_l_cpcs_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogL_cpcsU_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cog_cogL_cpcsL_mean[1,1]) `star_cog_cogL_cpcsL' " & " %02.0f ( n_cog_l_cpcs_l ) " & " %02.0f ( n_clust_cog_l_cpcs_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogL_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. 
. file write `hh2' " CPCS score^{c -(}a{c )-} & Math Top 50\%  & CPCS Top 50\% & " %04.3f (cpcs_cogU_cpcsU_mean[1,1]) `star_cpcs_cogU_cpcsU' " & " %02.0f ( n_cpcs_u_cog_u ) " & " %02.0f ( n_clust_cpcs_u_cog_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cpcs_cogU_cpcsU_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cpcs_cogU_cpcsL_mean[1,1]) `star_cpcs_cogU_cpcsL' " & " %02.0f ( n_cpcs_l_cog_u ) " & " %02.0f ( n_clust_cpcs_l_cog_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &       & & ( " %04.3f (cpcs_cogU_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & Math Bottom 50\%  & CPCS Top 50\% & " %04.3f (cpcs_cogL_cpcsU_mean[1,1]) `star_cpcs_cogL_cpcsU' " & " %02.0f ( n_cpcs_u_cog_l ) " & " %02.0f ( n_clust_cpcs_u_cog_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cpcs_cogL_cpcsU_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cpcs_cogL_cpcsL_mean[1,1]) `star_cpcs_cogL_cpcsL' " & " %02.0f ( n_cpcs_l_cog_l ) " & " %02.0f ( n_clust_cpcs_l_cog_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &       & & ( " %04.3f (cpcs_cogL_cpcsL_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. 
. file write `hh2' "\\\midrule" _newline
{txt}
{com}. file write `hh2' "\end{c -(}tabular{c )-}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\item (a) Dependent variables are standardized using the average and variance of the whole follow-up sample in the February 2022 survey. " _newline
{txt}
{com}. file write `hh2' "\item (b) Cutoffs are created based on whether their ability to perform each item at the baseline was higher or lower than the median. " _newline
{txt}
{com}. file write `hh2' "\item (c) Wild cluster bootstrap p-values are reported within parentheses. Clusters are schools at the baseline." _newline
{txt}
{com}. file write `hh2' "\item (d) $^*$ Significant at 10\% level; $^{c -(}**{c )-}$ significant at 5\% level; $^{c -(}***{c )-}$ significant at 1\% level. " _newline
{txt}
{com}. file write `hh2' "\end{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}threeparttable{c )-}" _newline
{txt}
{com}. file write `hh2' "{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:addlabel{c )-}%" _newline
{txt}
{com}. file write `hh2' "\end{c -(}table{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. file close `hh2'
{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/mb/s9qljd594gbfyhl2dqnnwlcw0000gn/T//SD56186.000000"
{txt}
{com}. * This is the do file to create "Table 5. Heterogeneity by Baseline Abilites (Math and CPCS)"
. set seed 11111
{txt}
{com}. 
. use "$path_data/temp/followup_student_baseline_score_missing_dummy", replace
{txt}
{com}. 
. egen DT_score_pre_std_med = median(DT_score_pre_std)
{txt}
{com}. gen DT_score_pre_std_upper50 = 1 if DT_score_pre_std>DT_score_pre_std_med
{txt}(121 missing values generated)

{com}. recode DT_score_pre_std_upper50 (.=0)
{txt}(121 changes made to {bf:DT_score_pre_std_upper50})

{com}. 
. egen rosen_pre_std_med = median(rosen_pre_std)
{txt}
{com}. gen rosen_pre_std_upper50 = 1 if rosen_pre_std>rosen_pre_std_med
{txt}(128 missing values generated)

{com}. recode rosen_pre_std_upper50 (.=0)
{txt}(128 changes made to {bf:rosen_pre_std_upper50})

{com}. rename rosen_pre_std_upper50 RSES_std_upper50
{res}{txt}
{com}. 
. egen cpcs_pre_std_med = median(cpcs_pre_std)
{txt}
{com}. gen cpcs_pre_std_upper50 = 1 if cpcs_pre_std>cpcs_pre_std_med
{txt}(135 missing values generated)

{com}. recode cpcs_pre_std_upper50 (.=0)
{txt}(135 changes made to {bf:cpcs_pre_std_upper50})

{com}. rename cpcs_pre_std_upper50 CPCS_std_upper50
{res}{txt}
{com}. 
. 
. /// Cognitive
> wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 59
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 22
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2397567{col 38}{space 1}  -0.86{col 46}{space 3}0.442{col 54}{space 3}-.8406214{col 66}{space 3} .4161941
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_rses_u = e(N)
{txt}
{com}. scalar n_clust_cog_u_rses_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.3459862{col 38}{space 1}  -1.14{col 46}{space 3}0.272{col 54}{space 3}-.9390312{col 66}{space 3} .2653954
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_rses_l = e(N)
{txt}
{com}. scalar n_clust_cog_u_rses_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2419353{col 38}{space 1}  -0.87{col 46}{space 3}0.376{col 54}{space 3}-.8813633{col 66}{space 3} .3970286
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_rses_u = e(N)
{txt}
{com}. scalar n_clust_cog_l_rses_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0781293{col 38}{space 1}  -0.29{col 46}{space 3}0.782{col 54}{space 3} -.633589{col 66}{space 3} .5514957
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_rses_l = e(N)
{txt}
{com}. scalar n_clust_cog_l_rses_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 55
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0533319{col 38}{space 1}   0.17{col 46}{space 3}0.882{col 54}{space 3}-.6611625{col 66}{space 3}  .806006
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_cpcs_u = e(N)
{txt}
{com}. scalar n_clust_cog_u_cpcs_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 67
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.5660393{col 38}{space 1}  -1.84{col 46}{space 3}0.106{col 54}{space 3}-1.193385{col 66}{space 3} .1454137
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_cpcs_l = e(N)
{txt}
{com}. scalar n_clust_cog_u_cpcs_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0295186{col 38}{space 1}   0.10{col 46}{space 3}0.894{col 54}{space 3}-.5566965{col 66}{space 3} .7313526
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_cpcs_u = e(N)
{txt}
{com}. scalar n_clust_cog_l_cpcs_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 68
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  -.19243{col 38}{space 1}  -0.73{col 46}{space 3}0.530{col 54}{space 3}-.7624104{col 66}{space 3} .3217706
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_cpcs_l = e(N)
{txt}
{com}. scalar n_clust_cog_l_cpcs_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. 
. 
. /// RSES
> wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .9579135{col 38}{space 1}   3.36{col 46}{space 3}0.006{col 54}{space 3} .2995461{col 66}{space 3}  1.61595
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_u_cog_u = e(N)
{txt}
{com}. scalar n_clust_rses_u_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 61
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0615952{col 38}{space 1}   0.32{col 46}{space 3}0.772{col 54}{space 3}-.3396273{col 66}{space 3} .4674283
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_l_cog_u = e(N)
{txt}
{com}. scalar n_clust_rses_l_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .4920182{col 38}{space 1}   1.44{col 46}{space 3}0.196{col 54}{space 3}-.3089123{col 66}{space 3} 1.207425
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_u_cog_l = e(N)
{txt}
{com}. scalar n_clust_rses_u_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1388312{col 38}{space 1}   0.42{col 46}{space 3}0.754{col 54}{space 3}-.6694186{col 66}{space 3}   .96379
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_l_cog_l = e(N)
{txt}
{com}. scalar n_clust_rses_l_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 52
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 20
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .6664341{col 38}{space 1}   3.26{col 46}{space 3}0.006{col 54}{space 3} .1980499{col 66}{space 3} 1.114933
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3847293{col 38}{space 1}   1.53{col 46}{space 3}0.132{col 54}{space 3} -.142619{col 66}{space 3} .9763969
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2062219{col 38}{space 1}   0.47{col 46}{space 3}0.648{col 54}{space 3}-.9251009{col 66}{space 3} 1.214517
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 66
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3332931{col 38}{space 1}   0.96{col 46}{space 3}0.396{col 54}{space 3}-.5699815{col 66}{space 3}  1.19557
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. 
. 
. /// CPCS
> 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} 1.020132{col 38}{space 1}   3.66{col 46}{space 3}0.006{col 54}{space 3} .4028772{col 66}{space 3}  1.72851
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 61
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0665639{col 38}{space 1}   0.34{col 46}{space 3}0.684{col 54}{space 3} -.332363{col 66}{space 3} .4531203
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:19})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .5458399{col 38}{space 1}   1.63{col 46}{space 3}0.150{col 54}{space 3}-.3241352{col 66}{space 3} 1.232792
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1431476{col 38}{space 1}   0.41{col 46}{space 3}0.738{col 54}{space 3}-.6865997{col 66}{space 3} 1.002436
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 52
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 20
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  .774167{col 38}{space 1}   3.68{col 46}{space 3}0.002{col 54}{space 3} .3584126{col 66}{space 3} 1.254922
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_u_cog_u = e(N)
{txt}
{com}. scalar n_clust_cpcs_u_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3575321{col 38}{space 1}   1.51{col 46}{space 3}0.148{col 54}{space 3}-.1602723{col 66}{space 3}  .881994
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_l_cog_u = e(N)
{txt}
{com}. scalar n_clust_cpcs_l_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2792052{col 38}{space 1}   0.63{col 46}{space 3}0.514{col 54}{space 3}-.8447186{col 66}{space 3} 1.281683
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_u_cog_l = e(N)
{txt}
{com}. scalar n_clust_cpcs_u_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 66
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}   .33148{col 38}{space 1}   0.95{col 46}{space 3}0.408{col 54}{space 3}-.4831461{col 66}{space 3} 1.121361
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_l_cog_l = e(N)
{txt}
{com}. scalar n_clust_cpcs_l_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. // significant level
. 
. 
. local outcome cog rses cpcs
{txt}
{com}. local hetero1 cogU cogL
{txt}
{com}. local hetero2 rsesU rsesL
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. foreach h1 in `hetero1'{c -(}
{txt}  3{com}. foreach h2 in `hetero2'{c -(}
{txt}  4{com}.                 if `dep'_`h1'_`h2'_pv[1,1]<=0.01 {c -(}
{txt}  5{com}.                         local star_`dep'_`h1'_`h2' %3s "***"
{txt}  6{com}.                 {c )-}
{txt}  7{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.01) & (`dep'_`h1'_`h2'_pv[1,1]<=0.05) {c -(}
{txt}  8{com}.                         local star_`dep'_`h1'_`h2' %2s "**"
{txt}  9{com}.                 {c )-}
{txt} 10{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.05) & (`dep'_`h1'_`h2'_pv[1,1]<=0.10) {c -(}
{txt} 11{com}.                         local star_`dep'_`h1'_`h2' %1s "*"
{txt} 12{com}.                 {c )-}
{txt} 13{com}.                 else {c -(}
{txt} 14{com}.                         local star_`dep'_`h1'_`h2'  ""
{txt} 15{com}.                 {c )-}
{txt} 16{com}. {c )-} 
{txt} 17{com}. {c )-}
{txt} 18{com}. {c )-}
{txt}
{com}. 
. local outcome cog rses cpcs
{txt}
{com}. local hetero1 cogU cogL
{txt}
{com}. local hetero2 cpcsU cpcsL
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. foreach h1 in `hetero1'{c -(}
{txt}  3{com}. foreach h2 in `hetero2'{c -(}
{txt}  4{com}.                 if `dep'_`h1'_`h2'_pv[1,1]<=0.01 {c -(}
{txt}  5{com}.                         local star_`dep'_`h1'_`h2' %3s "***"
{txt}  6{com}.                 {c )-}
{txt}  7{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.01) & (`dep'_`h1'_`h2'_pv[1,1]<=0.05) {c -(}
{txt}  8{com}.                         local star_`dep'_`h1'_`h2' %2s "**"
{txt}  9{com}.                 {c )-}
{txt} 10{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.05) & (`dep'_`h1'_`h2'_pv[1,1]<=0.10) {c -(}
{txt} 11{com}.                         local star_`dep'_`h1'_`h2' %1s "*"
{txt} 12{com}.                 {c )-}
{txt} 13{com}.                 else {c -(}
{txt} 14{com}.                         local star_`dep'_`h1'_`h2'  ""
{txt} 15{com}.                 {c )-}
{txt} 16{com}. {c )-} 
{txt} 17{com}. {c )-}
{txt} 18{com}. {c )-}
{txt}
{com}. 
. /// Table
> 
. tempname hh2
{txt}
{com}. file open `hh2' using "$path_output/hetero_2by2_CPCS.tex", write replace
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "% Author: Kazuma Takakura" _newline
{txt}
{com}. file write `hh2' "% Date: `c(current_date)'" _newline
{txt}
{com}. file write `hh2' "% Time: `c(current_time)'" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. 
. file write `hh2' "\begin{c -(}table{c )-}[h!]\footnotesize" _newline
{txt}
{com}. file write `hh2' "  \centering" _newline
{txt}
{com}. file write `hh2' "  \caption{c -(}Heterogeneity among Baseline Abilites (Math and CPCS){c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:hetero_cpcs{c )-}" _newline
{txt}
{com}. file write `hh2' "\scalebox{c -(}1{c )-}{c -(}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}threeparttable{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "\begin{c -(}tabular{c )-}{c -(}lccccccc{c )-}\toprule" _newline
{txt}
{com}. 
. file write `hh2' " Dependent Variable & \multicolumn{c -(}2{c )-}{c -(}c{c )-}{c -(}Baseline^{c -(}b{c )-}{c )-} & Difference & Obs & N of clusters  \\\midrule\midrule" _newline
{txt}
{com}. file write `hh2' " Rapid math test score^{c -(}a{c )-} & Math Top 50\%  & CPCS Top 50\% & " %04.3f (cog_cogU_rsesU_mean[1,1]) `star_cog_cogU_cpcsU' " & " %02.0f ( n_cog_u_cpcs_u ) " & " %02.0f ( n_clust_cog_u_cpcs_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogU_cpcsU_pv[1,1]) " )  & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cog_cogU_cpcsL_mean[1,1]) `star_cog_cogU_cpcsL' " & " %02.0f ( n_cog_u_cpcs_l ) " & " %02.0f ( n_clust_cog_u_cpcs_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogU_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & Math Bottom 50\%  & CPCS Top 50\% & " %04.3f (cog_cogL_cpcsU_mean[1,1]) `star_cog_cogL_cpcsU' " & " %02.0f ( n_cog_l_cpcs_u ) " & " %02.0f ( n_clust_cog_l_cpcs_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogL_cpcsU_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cog_cogL_cpcsL_mean[1,1]) `star_cog_cogL_cpcsL' " & " %02.0f ( n_cog_l_cpcs_l ) " & " %02.0f ( n_clust_cog_l_cpcs_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogL_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. 
. file write `hh2' " CPCS score^{c -(}a{c )-} & Math Top 50\%  & CPCS Top 50\% & " %04.3f (cpcs_cogU_cpcsU_mean[1,1]) `star_cpcs_cogU_cpcsU' " & " %02.0f ( n_cpcs_u_cog_u ) " & " %02.0f ( n_clust_cpcs_u_cog_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cpcs_cogU_cpcsU_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cpcs_cogU_cpcsL_mean[1,1]) `star_cpcs_cogU_cpcsL' " & " %02.0f ( n_cpcs_l_cog_u ) " & " %02.0f ( n_clust_cpcs_l_cog_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &       & & ( " %04.3f (cpcs_cogU_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & Math Bottom 50\%  & CPCS Top 50\% & " %04.3f (cpcs_cogL_cpcsU_mean[1,1]) `star_cpcs_cogL_cpcsU' " & " %02.0f ( n_cpcs_u_cog_l ) " & " %02.0f ( n_clust_cpcs_u_cog_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cpcs_cogL_cpcsU_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cpcs_cogL_cpcsL_mean[1,1]) `star_cpcs_cogL_cpcsL' " & " %02.0f ( n_cpcs_l_cog_l ) " & " %02.0f ( n_clust_cpcs_l_cog_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &       & & ( " %04.3f (cpcs_cogL_cpcsL_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. 
. file write `hh2' "\\\midrule" _newline
{txt}
{com}. file write `hh2' "\end{c -(}tabular{c )-}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\item (a) Dependent variables are standardized using the average and variance of the whole follow-up sample in the February 2022 survey. " _newline
{txt}
{com}. file write `hh2' "\item (b) Cutoffs are created based on whether their ability to perform each item at the baseline was higher or lower than the median. " _newline
{txt}
{com}. file write `hh2' "\item (c) Wild cluster bootstrap p-values are reported within parentheses. Clusters are schools at the baseline." _newline
{txt}
{com}. file write `hh2' "\item (d) $^*$ Significant at 10\% level; $^{c -(}**{c )-}$ significant at 5\% level; $^{c -(}***{c )-}$ significant at 1\% level. " _newline
{txt}
{com}. file write `hh2' "\end{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}threeparttable{c )-}" _newline
{txt}
{com}. file write `hh2' "{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:addlabel{c )-}%" _newline
{txt}
{com}. file write `hh2' "\end{c -(}table{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. file close `hh2'
{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/mb/s9qljd594gbfyhl2dqnnwlcw0000gn/T//SD56186.000000"
{txt}
{com}. * This is the do file to create "Table 5. Heterogeneity by Baseline Abilites (Math and CPCS)"
. set seed 123
{txt}
{com}. 
. use "$path_data/temp/followup_student_baseline_score_missing_dummy", replace
{txt}
{com}. 
. egen DT_score_pre_std_med = median(DT_score_pre_std)
{txt}
{com}. gen DT_score_pre_std_upper50 = 1 if DT_score_pre_std>DT_score_pre_std_med
{txt}(121 missing values generated)

{com}. recode DT_score_pre_std_upper50 (.=0)
{txt}(121 changes made to {bf:DT_score_pre_std_upper50})

{com}. 
. egen rosen_pre_std_med = median(rosen_pre_std)
{txt}
{com}. gen rosen_pre_std_upper50 = 1 if rosen_pre_std>rosen_pre_std_med
{txt}(128 missing values generated)

{com}. recode rosen_pre_std_upper50 (.=0)
{txt}(128 changes made to {bf:rosen_pre_std_upper50})

{com}. rename rosen_pre_std_upper50 RSES_std_upper50
{res}{txt}
{com}. 
. egen cpcs_pre_std_med = median(cpcs_pre_std)
{txt}
{com}. gen cpcs_pre_std_upper50 = 1 if cpcs_pre_std>cpcs_pre_std_med
{txt}(135 missing values generated)

{com}. recode cpcs_pre_std_upper50 (.=0)
{txt}(135 changes made to {bf:cpcs_pre_std_upper50})

{com}. rename cpcs_pre_std_upper50 CPCS_std_upper50
{res}{txt}
{com}. 
. 
. /// Cognitive
> wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 59
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 22
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2397567{col 38}{space 1}  -0.86{col 46}{space 3}0.444{col 54}{space 3} -.835023{col 66}{space 3} .4309843
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_rses_u = e(N)
{txt}
{com}. scalar n_clust_cog_u_rses_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.3459862{col 38}{space 1}  -1.14{col 46}{space 3}0.246{col 54}{space 3}-.9646946{col 66}{space 3} .2967857
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_rses_l = e(N)
{txt}
{com}. scalar n_clust_cog_u_rses_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2419353{col 38}{space 1}  -0.87{col 46}{space 3}0.390{col 54}{space 3}-.8433267{col 66}{space 3} .3920268
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_rses_u = e(N)
{txt}
{com}. scalar n_clust_cog_l_rses_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0781293{col 38}{space 1}  -0.29{col 46}{space 3}0.794{col 54}{space 3}-.6038382{col 66}{space 3} .5443816
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_rses_l = e(N)
{txt}
{com}. scalar n_clust_cog_l_rses_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 55
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0533319{col 38}{space 1}   0.17{col 46}{space 3}0.872{col 54}{space 3}-.6242397{col 66}{space 3} .7907538
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_cpcs_u = e(N)
{txt}
{com}. scalar n_clust_cog_u_cpcs_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 67
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.5660393{col 38}{space 1}  -1.84{col 46}{space 3}0.092{col 54}{space 3}-1.245036{col 66}{space 3} .1419984
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u_cpcs_l = e(N)
{txt}
{com}. scalar n_clust_cog_u_cpcs_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0295186{col 38}{space 1}   0.10{col 46}{space 3}0.942{col 54}{space 3}-.6165229{col 66}{space 3} .6514795
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_cpcs_u = e(N)
{txt}
{com}. scalar n_clust_cog_l_cpcs_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 68
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  -.19243{col 38}{space 1}  -0.73{col 46}{space 3}0.480{col 54}{space 3}-.7462179{col 66}{space 3} .3675341
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l_cpcs_l = e(N)
{txt}
{com}. scalar n_clust_cog_l_cpcs_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cog_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cog_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cog_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cog_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. 
. 
. /// RSES
> wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .9579135{col 38}{space 1}   3.36{col 46}{space 3}0.000{col 54}{space 3} .3158411{col 66}{space 3} 1.651529
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_u_cog_u = e(N)
{txt}
{com}. scalar n_clust_rses_u_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 61
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0615952{col 38}{space 1}   0.32{col 46}{space 3}0.748{col 54}{space 3} -.335182{col 66}{space 3} .4398791
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_l_cog_u = e(N)
{txt}
{com}. scalar n_clust_rses_l_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:29})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .4920182{col 38}{space 1}   1.44{col 46}{space 3}0.152{col 54}{space 3}-.1956882{col 66}{space 3} 1.132075
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_u_cog_l = e(N)
{txt}
{com}. scalar n_clust_rses_u_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1388312{col 38}{space 1}   0.42{col 46}{space 3}0.748{col 54}{space 3}-.6586927{col 66}{space 3} .8819884
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_rses_l_cog_l = e(N)
{txt}
{com}. scalar n_clust_rses_l_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 52
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 20
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .6664341{col 38}{space 1}   3.26{col 46}{space 3}0.004{col 54}{space 3} .2256924{col 66}{space 3}  1.16988
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3847293{col 38}{space 1}   1.53{col 46}{space 3}0.150{col 54}{space 3}-.1439999{col 66}{space 3} .9723153
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2062219{col 38}{space 1}   0.47{col 46}{space 3}0.630{col 54}{space 3}-.9392839{col 66}{space 3} 1.108811
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg RSES_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 66
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3332931{col 38}{space 1}   0.96{col 46}{space 3}0.416{col 54}{space 3}-.4864367{col 66}{space 3} 1.101715
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix rses_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix rses_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix rses_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix rses_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. 
. 
. /// CPCS
> 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} 1.020132{col 38}{space 1}   3.66{col 46}{space 3}0.002{col 54}{space 3} .4199485{col 66}{space 3} 1.782028
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 61
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0665639{col 38}{space 1}   0.34{col 46}{space 3}0.706{col 54}{space 3}-.3338298{col 66}{space 3} .4994486
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 56
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 21
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .5458399{col 38}{space 1}   1.63{col 46}{space 3}0.112{col 54}{space 3}-.2035501{col 66}{space 3} 1.209728
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_rsesU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_rsesU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_rsesU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_rsesU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & RSES_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 63
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1431476{col 38}{space 1}   0.41{col 46}{space 3}0.700{col 54}{space 3}-.6565782{col 66}{space 3} 1.032317
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_rsesL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_rsesL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_rsesL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_rsesL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:19})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 52
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 20
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.6
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  .774167{col 38}{space 1}   3.68{col 46}{space 3}0.006{col 54}{space 3}  .320576{col 66}{space 3} 1.297191
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_u_cog_u = e(N)
{txt}
{com}. scalar n_clust_cpcs_u_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 1 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 65
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.7
{col 69}{txt}max{col 72} = {res}  7
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3575321{col 38}{space 1}   1.51{col 46}{space 3}0.154{col 54}{space 3} -.150128{col 66}{space 3} .9146968
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_l_cog_u = e(N)
{txt}
{com}. scalar n_clust_cpcs_l_cog_u = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogU_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogU_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogU_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogU_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 53
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 23
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.3
{col 69}{txt}max{col 72} = {res}  9
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2792052{col 38}{space 1}   0.63{col 46}{space 3}0.546{col 54}{space 3}-.9224898{col 66}{space 3} 1.348458
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_u_cog_l = e(N)
{txt}
{com}. scalar n_clust_cpcs_u_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_cpcsU_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_cpcsU_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_cpcsU_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_cpcsU_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg CPCS_std treatment if DT_score_pre_std_upper50 == 0 & CPCS_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res} 66
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 24
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}2.8
{col 69}{txt}max{col 72} = {res}  8
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}   .33148{col 38}{space 1}   0.95{col 46}{space 3}0.420{col 54}{space 3}-.5052153{col 66}{space 3} 1.053721
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cpcs_l_cog_l = e(N)
{txt}
{com}. scalar n_clust_cpcs_l_cog_l = e(N_clust)
{txt}
{com}. matrix temp = r(table)
{txt}
{com}. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix cpcs_cogL_cpcsL_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix cpcs_cogL_cpcsL_mean[1,`j'] = temp[1,`j']
{txt}  3{com}. * standard error
. * matrix cpcs_cogL_cpcsL_se[1,`j'] = temp[2,`j']
. * p value
. matrix cpcs_cogL_cpcsL_pv[1,`j'] = temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. // significant level
. 
. 
. local outcome cog rses cpcs
{txt}
{com}. local hetero1 cogU cogL
{txt}
{com}. local hetero2 rsesU rsesL
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. foreach h1 in `hetero1'{c -(}
{txt}  3{com}. foreach h2 in `hetero2'{c -(}
{txt}  4{com}.                 if `dep'_`h1'_`h2'_pv[1,1]<=0.01 {c -(}
{txt}  5{com}.                         local star_`dep'_`h1'_`h2' %3s "***"
{txt}  6{com}.                 {c )-}
{txt}  7{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.01) & (`dep'_`h1'_`h2'_pv[1,1]<=0.05) {c -(}
{txt}  8{com}.                         local star_`dep'_`h1'_`h2' %2s "**"
{txt}  9{com}.                 {c )-}
{txt} 10{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.05) & (`dep'_`h1'_`h2'_pv[1,1]<=0.10) {c -(}
{txt} 11{com}.                         local star_`dep'_`h1'_`h2' %1s "*"
{txt} 12{com}.                 {c )-}
{txt} 13{com}.                 else {c -(}
{txt} 14{com}.                         local star_`dep'_`h1'_`h2'  ""
{txt} 15{com}.                 {c )-}
{txt} 16{com}. {c )-} 
{txt} 17{com}. {c )-}
{txt} 18{com}. {c )-}
{txt}
{com}. 
. local outcome cog rses cpcs
{txt}
{com}. local hetero1 cogU cogL
{txt}
{com}. local hetero2 cpcsU cpcsL
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. foreach h1 in `hetero1'{c -(}
{txt}  3{com}. foreach h2 in `hetero2'{c -(}
{txt}  4{com}.                 if `dep'_`h1'_`h2'_pv[1,1]<=0.01 {c -(}
{txt}  5{com}.                         local star_`dep'_`h1'_`h2' %3s "***"
{txt}  6{com}.                 {c )-}
{txt}  7{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.01) & (`dep'_`h1'_`h2'_pv[1,1]<=0.05) {c -(}
{txt}  8{com}.                         local star_`dep'_`h1'_`h2' %2s "**"
{txt}  9{com}.                 {c )-}
{txt} 10{com}.                 else if (`dep'_`h1'_`h2'_pv[1,1]>0.05) & (`dep'_`h1'_`h2'_pv[1,1]<=0.10) {c -(}
{txt} 11{com}.                         local star_`dep'_`h1'_`h2' %1s "*"
{txt} 12{com}.                 {c )-}
{txt} 13{com}.                 else {c -(}
{txt} 14{com}.                         local star_`dep'_`h1'_`h2'  ""
{txt} 15{com}.                 {c )-}
{txt} 16{com}. {c )-} 
{txt} 17{com}. {c )-}
{txt} 18{com}. {c )-}
{txt}
{com}. 
. /// Table
> 
. tempname hh2
{txt}
{com}. file open `hh2' using "$path_output/hetero_2by2_CPCS.tex", write replace
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "% Author: Kazuma Takakura" _newline
{txt}
{com}. file write `hh2' "% Date: `c(current_date)'" _newline
{txt}
{com}. file write `hh2' "% Time: `c(current_time)'" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. 
. file write `hh2' "\begin{c -(}table{c )-}[h!]\footnotesize" _newline
{txt}
{com}. file write `hh2' "  \centering" _newline
{txt}
{com}. file write `hh2' "  \caption{c -(}Heterogeneity among Baseline Abilites (Math and CPCS){c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:hetero_cpcs{c )-}" _newline
{txt}
{com}. file write `hh2' "\scalebox{c -(}1{c )-}{c -(}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}threeparttable{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "\begin{c -(}tabular{c )-}{c -(}lccccccc{c )-}\toprule" _newline
{txt}
{com}. 
. file write `hh2' " Dependent Variable & \multicolumn{c -(}2{c )-}{c -(}c{c )-}{c -(}Baseline^{c -(}b{c )-}{c )-} & Difference & Obs & N of clusters  \\\midrule\midrule" _newline
{txt}
{com}. file write `hh2' " Rapid math test score^{c -(}a{c )-} & Math Top 50\%  & CPCS Top 50\% & " %04.3f (cog_cogU_rsesU_mean[1,1]) `star_cog_cogU_cpcsU' " & " %02.0f ( n_cog_u_cpcs_u ) " & " %02.0f ( n_clust_cog_u_cpcs_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogU_cpcsU_pv[1,1]) " )  & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cog_cogU_cpcsL_mean[1,1]) `star_cog_cogU_cpcsL' " & " %02.0f ( n_cog_u_cpcs_l ) " & " %02.0f ( n_clust_cog_u_cpcs_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogU_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & Math Bottom 50\%  & CPCS Top 50\% & " %04.3f (cog_cogL_cpcsU_mean[1,1]) `star_cog_cogL_cpcsU' " & " %02.0f ( n_cog_l_cpcs_u ) " & " %02.0f ( n_clust_cog_l_cpcs_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogL_cpcsU_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cog_cogL_cpcsL_mean[1,1]) `star_cog_cogL_cpcsL' " & " %02.0f ( n_cog_l_cpcs_l ) " & " %02.0f ( n_clust_cog_l_cpcs_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cog_cogL_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. 
. file write `hh2' " CPCS score^{c -(}a{c )-} & Math Top 50\%  & CPCS Top 50\% & " %04.3f (cpcs_cogU_cpcsU_mean[1,1]) `star_cpcs_cogU_cpcsU' " & " %02.0f ( n_cpcs_u_cog_u ) " & " %02.0f ( n_clust_cpcs_u_cog_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cpcs_cogU_cpcsU_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cpcs_cogU_cpcsL_mean[1,1]) `star_cpcs_cogU_cpcsL' " & " %02.0f ( n_cpcs_l_cog_u ) " & " %02.0f ( n_clust_cpcs_l_cog_u ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &       & & ( " %04.3f (cpcs_cogU_cpcsL_pv[1,1]) " )  & &  \\ " _newline
{txt}
{com}. file write `hh2' "  & Math Bottom 50\%  & CPCS Top 50\% & " %04.3f (cpcs_cogL_cpcsU_mean[1,1]) `star_cpcs_cogL_cpcsU' " & " %02.0f ( n_cpcs_u_cog_l ) " & " %02.0f ( n_clust_cpcs_u_cog_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      & & & ( " %04.3f (cpcs_cogL_cpcsU_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. file write `hh2' "  & & CPCS Bottom 50\% & " %04.3f (cpcs_cogL_cpcsL_mean[1,1]) `star_cpcs_cogL_cpcsL' " & " %02.0f ( n_cpcs_l_cog_l ) " & " %02.0f ( n_clust_cpcs_l_cog_l ) " \\ " _newline
{txt}
{com}. file write `hh2' "                      &       & & ( " %04.3f (cpcs_cogL_cpcsL_pv[1,1]) " ) & &   \\ " _newline
{txt}
{com}. 
. file write `hh2' "\\\midrule" _newline
{txt}
{com}. file write `hh2' "\end{c -(}tabular{c )-}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\item (a) Dependent variables are standardized using the average and variance of the whole follow-up sample in the February 2022 survey. " _newline
{txt}
{com}. file write `hh2' "\item (b) Cutoffs are created based on whether their ability to perform each item at the baseline was higher or lower than the median. " _newline
{txt}
{com}. file write `hh2' "\item (c) Wild cluster bootstrap p-values are reported within parentheses. Clusters are schools at the baseline." _newline
{txt}
{com}. file write `hh2' "\item (d) $^*$ Significant at 10\% level; $^{c -(}**{c )-}$ significant at 5\% level; $^{c -(}***{c )-}$ significant at 1\% level. " _newline
{txt}
{com}. file write `hh2' "\end{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}threeparttable{c )-}" _newline
{txt}
{com}. file write `hh2' "{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:addlabel{c )-}%" _newline
{txt}
{com}. file write `hh2' "\end{c -(}table{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. file close `hh2'
{txt}
{com}. 
{txt}end of do-file

{com}. do "/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/do_files/2_table_1.do"
{txt}
{com}. * This is the do file to create "Table 1. Summary Statistics"
. set seed 123
{txt}
{com}. 
. use "$path_data/temp/followup_student_parents_matched", clear
{txt}
{com}. 
. corr rosen_pre_std cpcs_pre_std
{txt}(obs=243)

             {c |} rosen_~d cpcs_p~d
{hline 13}{c +}{hline 18}
rosen_pre_~d {c |}{res}   1.0000
{txt}cpcs_pre_std {c |}{res}   0.9026   1.0000

{txt}
{com}. corr RSES_std CPCS_std
{txt}(obs=236)

             {c |} RSES_std CPCS_std
{hline 13}{c +}{hline 18}
    RSES_std {c |}{res}   1.0000
    {txt}CPCS_std {c |}{res}   0.9701   1.0000

{txt}
{com}. 
. 
. /// Varable Selection
> /// Baseline
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat DT_score_pre_std rosen_pre_std cpcs_pre_std if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_bl = r(StatTotal)
{txt}  5{com}. 
. tabstat DT_score_pre_std rosen_pre_std cpcs_pre_std if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_bl = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}      144       145       145
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   DT_score_p~d  rosen_pre_~d  cpcs_pre_std
N {res}          144           145           145
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}       95        98        98
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   DT_score_p~d  rosen_pre_~d  cpcs_pre_std
N {res}           95            98            98
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res}-.0313509  .0382918  .1345164
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      DT_score_p~d  rosen_pre_~d  cpcs_pre_std
Mean {res}   -.03135095     .03829184      .1345164
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .0475214 -.0566567 -.1990291
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      DT_score_p~d  rosen_pre_~d  cpcs_pre_std
Mean {res}    .04752144    -.05665667    -.19902912
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} 1.023177  .9748496  .9271749
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    DT_score_p~d  rosen_pre_~d  cpcs_pre_std
SD {res}    1.0231772     .97484957     .92717486
{reset}
   Stats {...}
{c |}{...}
  DT_sco~d  rosen_~d  cpcs_p~d
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} .9672202  1.038561  1.073121
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    DT_score_p~d  rosen_pre_~d  cpcs_pre_std
SD {res}    .96722024      1.038561     1.0731214
{reset}
{com}. 
. matrix n_bl = J(1,3,.)
{txt}
{com}. forvalues i = 1/3 {c -(}
{txt}  2{com}.         matrix n_bl[1,`i'] = n_tr_bl[1,`i'] + n_ct_bl[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in DT_score_pre_std rosen_pre_std cpcs_pre_std{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}239
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 32
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  2
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.5
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        DT_score_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0788724{col 38}{space 1}  -0.38{col 46}{space 3}0.726{col 54}{space 3}-.5170202{col 66}{space 3} .3849625
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}           rosen_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0949485{col 38}{space 1}   0.47{col 46}{space 3}0.600{col 54}{space 3}-.3528281{col 66}{space 3} .5243389
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}            cpcs_pre_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3335455{col 38}{space 1}   1.82{col 46}{space 3}0.116{col 54}{space 3}-.0763347{col 66}{space 3} .7082129
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. /// Family
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat hhmember hhheadage hhheadeduyear if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_parent = r(StatTotal)
{txt}  5{com}. 
. tabstat hhmember hhheadage hhheadeduyear if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_parent = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}      145       145       145
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
       hhmember     hhheadage  hhheadeduy~r
N {res}          145           145           145
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}       98        98        98
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
       hhmember     hhheadage  hhheadeduy~r
N {res}           98            98            98
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res} 4.510345  46.57241  2.331034
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
          hhmember     hhheadage  hhheadeduy~r
Mean {res}    4.5103448     46.572414     2.3310345
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res} 4.265306  46.68878  3.163265
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
          hhmember     hhheadage  hhheadeduy~r
Mean {res}    4.2653061     46.688776     3.1632653
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} 1.280827   9.03907  2.995495
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
        hhmember     hhheadage  hhheadeduy~r
SD {res}    1.2808268     9.0390702     2.9954947
{reset}
   Stats {...}
{c |}{...}
  hhmember  hhhead~e  hhhead~r
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} 1.197515  9.408681  3.530993
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
        hhmember     hhheadage  hhheadeduy~r
SD {res}    1.1975148     9.4086808     3.5309935
{reset}
{com}. 
. matrix n_parent = J(1,3,.)
{txt}
{com}. forvalues i = 1/3 {c -(}
{txt}  2{com}.         matrix n_parent[1,`i'] = n_tr_parent[1,`i'] + n_ct_parent[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in hhmember hhheadage hhheadeduyear{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                hhmember{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .2450387{col 38}{space 1}   1.29{col 46}{space 3}0.190{col 54}{space 3}-.1378142{col 66}{space 3} .7032024
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}               hhheadage{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.1163617{col 38}{space 1}  -0.07{col 46}{space 3}0.914{col 54}{space 3}-3.252753{col 66}{space 3} 3.440111
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}           hhheadeduyear{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.8322308{col 38}{space 1}  -2.22{col 46}{space 3}0.018{col 54}{space 3}-1.589777{col 66}{space 3}-.0728916
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. 
. 
. /// School　attendance
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat q2a q2b q2c q2h if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_school = r(StatTotal)
{txt}  5{com}. 
. tabstat q2a q2b q2c q2h if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_school = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:N} {...}
{c |}{...}
 {res}      145       145       145       145
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
   q2a  q2b  q2c  q2h
N {res} 145  145  145  145
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:N} {...}
{c |}{...}
 {res}       98        98        98        98
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
   q2a  q2b  q2c  q2h
N {res}  98   98   98   98
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .5517241  9.606897   .062069  .3793103
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
            q2a        q2b        q2c        q2h
Mean {res} .55172414  9.6068966  .06206897  .37931034
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .5306122  9.602041  .0408163  .4489796
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
            q2a        q2b        q2c        q2h
Mean {res} .53061224  9.6020408  .04081633  .44897959
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:SD} {...}
{c |}{...}
 {res} .4990412  1.029405  .2421171  .4868973
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
          q2a        q2b        q2c        q2h
SD {res} .49904123  1.0294048   .2421171  .48689728
{reset}
   Stats {...}
{c |}{...}
       q2a       q2b       q2c       q2h
{hline 9}{c +}{hline 40}
{ralign 8:SD} {...}
{c |}{...}
 {res} .5016279  .8703571  .1988818  .4999474
{txt}{hline 9}{c BT}{hline 40}
{res}
{txt}r(StatTotal)[1,4]
          q2a        q2b        q2c        q2h
SD {res}  .5016279  .87035715  .19888179   .4999474
{reset}
{com}. 
. matrix n_school = J(1,4,.)
{txt}
{com}. forvalues i = 1/4 {c -(}
{txt}  2{com}.         matrix n_school[1,`i'] = n_tr_school[1,`i'] + n_ct_school[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in q2a q2b q2c q2h{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                     q2a{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0211119{col 38}{space 1}   0.25{col 46}{space 3}0.868{col 54}{space 3}-.1592541{col 66}{space 3} .2083223
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                     q2b{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0048557{col 38}{space 1}   0.03{col 46}{space 3}0.990{col 54}{space 3}-.3899922{col 66}{space 3}  .336345
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                     q2c{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0212526{col 38}{space 1}   0.56{col 46}{space 3}0.662{col 54}{space 3}-.0533022{col 66}{space 3} .0982309
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                     q2h{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0696692{col 38}{space 1}  -0.85{col 46}{space 3}0.446{col 54}{space 3}-.2494278{col 66}{space 3} .1087737
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. /// Other study variable
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat tutor study_other study_affect_covid hometutoring onlineclass studymyself parentsteach if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_study = r(StatTotal)
{txt}  5{com}. 
. tabstat tutor study_other study_affect_covid hometutoring onlineclass studymyself parentsteach if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_study = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:N} {...}
{c |}{...}
 {res}      145       145       145       145       145       145       145
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
          tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
N {res}          145           145           145           145           145           145           145
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:N} {...}
{c |}{...}
 {res}       98        98        98        98        98        98        98
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
          tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
N {res}           98            98            98            98            98            98            98
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:Mean} {...}
{c |}{...}
 {res}  .337931   .462069  .6482759  .0965517  .0482759  .5241379  .0275862
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
             tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
Mean {res}    .33793103     .46206897     .64827586     .09655172     .04827586     .52413793     .02758621
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .4591837  .4285714  .6020408  .1428571  .1326531  .5306122  .1938776
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
             tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
Mean {res}    .45918367     .42857143     .60204082     .14285714     .13265306     .53061224     .19387755
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:SD} {...}
{c |}{...}
 {res} .4746445  .5002873  .4791635  .2963701  .2150915  .5011481  .1643517
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
           tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
SD {res}    .47464445     .50028727     .47916354     .29637012     .21509153     .50114811     .16435174
{reset}
   Stats {...}
{c |}{...}
     tutor  study_~r  study_~d  hometu~g  online~s  studym~f  parent~h
{hline 9}{c +}{hline 70}
{ralign 8:SD} {...}
{c |}{...}
 {res} .5008934   .497416  .4919935  .3517262  .3409434  .5016279  .3973667
{txt}{hline 9}{c BT}{hline 70}
{res}
{txt}r(StatTotal)[1,7]
           tutor   study_other  study_affe~d  hometutoring   onlineclass   studymyself  parentsteach
SD {res}    .50089337       .497416     .49199354     .35172623     .34094336      .5016279     .39736667
{reset}
{com}. 
. matrix n_study = J(1,8,.)
{txt}
{com}. forvalues i = 1/8 {c -(}
{txt}  2{com}.         matrix n_study[1,`i'] = n_tr_study[1,`i'] + n_ct_study[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in tutor study_other study_affect_covid hometutoring onlineclass studymyself parentsteach{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                   tutor{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.1212526{col 38}{space 1}  -1.69{col 46}{space 3}0.120{col 54}{space 3}-.2773205{col 66}{space 3} .0421174
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}             study_other{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0334975{col 38}{space 1}   0.39{col 46}{space 3}0.742{col 54}{space 3}-.1488081{col 66}{space 3} .2186319
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}      study_affect_covid{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}  .046235{col 38}{space 1}   0.56{col 46}{space 3}0.576{col 54}{space 3} -.124707{col 66}{space 3} .2364771
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}            hometutoring{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0463054{col 38}{space 1}  -1.11{col 46}{space 3}0.312{col 54}{space 3} -.128302{col 66}{space 3} .0430425
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}             onlineclass{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0843772{col 38}{space 1}  -1.92{col 46}{space 3}0.096{col 54}{space 3} -.179885{col 66}{space 3} .0148069
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}             studymyself{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.0064743{col 38}{space 1}  -0.08{col 46}{space 3}0.940{col 54}{space 3}-.1754887{col 66}{space 3} .1699468
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}            parentsteach{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.1662913{col 38}{space 1}  -3.85{col 46}{space 3}0.000{col 54}{space 3} -.255542{col 66}{space 3}-.0629773
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. /// Cognitive
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat followup_cog_std if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_cog = r(StatTotal)
{txt}  5{com}. 
. tabstat followup_cog_std if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_cog = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}{ralign 12:Variable} {...}
{c |}         N
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res}      145
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
   followup_c~d
N {res}          145
{reset}
{ralign 12:Variable} {...}
{c |}         N
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res}       98
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
   followup_c~d
N {res}           98
{reset}
{ralign 12:Variable} {...}
{c |}      Mean
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res}-.0920409
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
      followup_c~d
Mean {res}   -.09204085
{reset}
{ralign 12:Variable} {...}
{c |}      Mean
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res} .1361831
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
      followup_c~d
Mean {res}    .13618309
{reset}
{ralign 12:Variable} {...}
{c |}        SD
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res} 1.070796
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
    followup_c~d
SD {res}     1.070796
{reset}
{ralign 12:Variable} {...}
{c |}        SD
{hline 13}{c +}{hline 10}
{ralign 12:followup_c~d} {...}
{c |}{...}
 {res} .8725076
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
    followup_c~d
SD {res}    .87250763
{reset}
{com}. 
. matrix n_cog = J(1,1,.)
{txt}
{com}. forvalues i = 1/1 {c -(}
{txt}  2{com}.         matrix n_cog[1,`i'] = n_tr_cog[1,`i'] + n_ct_cog[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std treatment, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2282239{col 38}{space 1}  -1.36{col 46}{space 3}0.192{col 54}{space 3}-.5930653{col 66}{space 3} .1293723
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}.         
. matrix r2_followup_cog_std_temp = r(table)
{txt}
{com}. 
. 
. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix r2_followup_cog_std_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix r2_followup_cog_std_mean[1,`j'] = r2_followup_cog_std_temp[1,`j']
{txt}  3{com}. * standard error
. * matrix r2_followup_cog_std_se[1,`j'] = r2_followup_cog_std_temp[2,`j']
. * p value
. matrix r2_followup_cog_std_pv[1,`j'] = r2_followup_cog_std_temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}.     
. /// Non cognitive
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat followup_noncog_std RSES_std CPCS_std if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_noncog = r(StatTotal)
{txt}  5{com}. 
. tabstat followup_noncog_std RSES_std CPCS_std if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_noncog = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}      105       140       140
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   followup_n~d      RSES_std      CPCS_std
N {res}          105           140           140
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:N} {...}
{c |}{...}
 {res}       74        96        96
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
   followup_n~d      RSES_std      CPCS_std
N {res}           74            96            96
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res} .1969319  .1591241  .1745941
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      followup_n~d      RSES_std      CPCS_std
Mean {res}    .19693189      .1591241     .17459415
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res}-.2794302 -.2320565  -.254617
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
      followup_n~d      RSES_std      CPCS_std
Mean {res}   -.27943024    -.23205648    -.25461705
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} 1.006158  1.022691  1.008304
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    followup_n~d      RSES_std      CPCS_std
SD {res}    1.0061577     1.0226907     1.0083041
{reset}
   Stats {...}
{c |}{...}
  follow..  RSES_std  CPCS_std
{hline 9}{c +}{hline 30}
{ralign 8:SD} {...}
{c |}{...}
 {res} .9279901  .9228443  .9357831
{txt}{hline 9}{c BT}{hline 30}
{res}
{txt}r(StatTotal)[1,3]
    followup_n~d      RSES_std      CPCS_std
SD {res}    .92799012     .92284427     .93578307
{reset}
{com}. 
. matrix n_noncog = J(1,3,.)
{txt}
{com}. forvalues i = 1/3 {c -(}
{txt}  2{com}.         matrix n_noncog[1,`i'] = n_tr_noncog[1,`i'] + n_ct_noncog[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in followup_noncog_std RSES_std CPCS_std{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}179
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 32
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}5.6
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}     followup_noncog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .4763621{col 38}{space 1}   2.08{col 46}{space 3}0.074{col 54}{space 3}-.0489589{col 66}{space 3} .9729653
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:28})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}236
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.2
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3911806{col 38}{space 1}   2.02{col 46}{space 3}0.064{col 54}{space 3}-.0321475{col 66}{space 3} .7806997
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}236
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.2
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .4292112{col 38}{space 1}   2.26{col 46}{space 3}0.038{col 54}{space 3} .0238533{col 66}{space 3} .7985476
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. 
. /// Behavioral
> foreach j in n mean sd {c -(}
{txt}  2{com}. tabstat hyper if treatment == 1, stat(`j') save
{txt}  3{com}. matrix list r(StatTotal)
{txt}  4{com}. matrix `j'_tr_hyper = r(StatTotal)
{txt}  5{com}. 
. tabstat hyper if treatment == 0, stat(`j') save
{txt}  6{com}. matrix list r(StatTotal)
{txt}  7{com}. matrix `j'_ct_hyper = r(StatTotal)
{txt}  8{com}. {c )-}

{txt}{ralign 12:Variable} {...}
{c |}         N
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res}      113
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
   hyper
N {res}   113
{reset}
{ralign 12:Variable} {...}
{c |}         N
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res}       71
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
   hyper
N {res}    71
{reset}
{ralign 12:Variable} {...}
{c |}      Mean
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res} .2654867
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
          hyper
Mean {res} .26548673
{reset}
{ralign 12:Variable} {...}
{c |}      Mean
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res} .0704225
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
          hyper
Mean {res} .07042254
{reset}
{ralign 12:Variable} {...}
{c |}        SD
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res}  .443559
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
        hyper
SD {res} .44355905
{reset}
{ralign 12:Variable} {...}
{c |}        SD
{hline 13}{c +}{hline 10}
{ralign 12:hyper} {...}
{c |}{...}
 {res} .2576789
{txt}{hline 13}{c BT}{hline 10}
{res}
{txt}symmetric r(StatTotal)[1,1]
        hyper
SD {res} .25767885
{reset}
{com}. 
. matrix n_hyper = J(1,1,.)
{txt}
{com}. forvalues i = 1/1 {c -(}
{txt}  2{com}.         matrix n_hyper[1,`i'] = n_tr_hyper[1,`i'] + n_ct_hyper[1,`i']
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach dep in hyper{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment if hypernoinfo == 0, cluster(school_no) reps(1000)
{txt}  3{com}.         
.         matrix r2_`dep'_temp = r(table)
{txt}  4{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  5{com}.                 matrix r2_`dep'_`s' = J(1,2,.)
{txt}  6{com}.         {c )-}
{txt}  7{com}. 
.         foreach j in 1 2 {c -(}
{txt}  8{com}.         * beta
.         matrix r2_`dep'_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt}  9{com}.         * standard error
.         * matrix r2_`dep'_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 10{com}.         {c )-}
{txt} 11{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}184
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}5.6
{col 69}{txt}max{col 72} = {res} 12
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                   hyper{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .1950642{col 38}{space 1}   3.37{col 46}{space 3}0.010{col 54}{space 3} .0645109{col 66}{space 3} .3241339
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. 
. // significant level
. 
. local outcome DT_score_pre_std rosen_pre_std cpcs_pre_std hhmember hhheadage hhheadeduyear q2a q2c q2h tutor study_other followup_cog_std RSES_std CPCS_std
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}.                 if r2_`dep'_pv[1,1]<=0.01 {c -(}
{txt}  3{com}.                         local star_`dep' %3s "***"
{txt}  4{com}.                 {c )-}
{txt}  5{com}.                 else if (r2_`dep'_pv[1,1]>0.01) & (r2_`dep'_pv[1,1]<=0.05) {c -(}
{txt}  6{com}.                         local star_`dep' %2s "**"
{txt}  7{com}.                 {c )-}
{txt}  8{com}.                 else if (r2_`dep'_pv[1,1]>0.05) & (r2_`dep'_pv[1,1]<=0.10) {c -(}
{txt}  9{com}.                         local star_`dep' %1s "*"
{txt} 10{com}.                 {c )-}
{txt} 11{com}.                 else {c -(}
{txt} 12{com}.                         local star_`dep'  ""
{txt} 13{com}.                 {c )-}
{txt} 14{com}. {c )-} 
{txt}
{com}. 
. rwolf DT_score_pre_std rosen_pre_std cpcs_pre_std hhmember hhheadage hhheadeduyear, indepvar(treatment) reps(1000)
Bootstrap replications (1000). This may take some time.
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Romano-Wolf step-down adjusted p-values


Independent variable:  treatment
Outcome variables:   DT_score_pre_std rosen_pre_std cpcs_pre_std hhmember
{col 22}hhheadage hhheadeduyear
Number of resamples: 1000


{hline 78}
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
{hline 20}+{hline 57}
   {txt}DT_score_pre_std {c |}    {res}0.5518             0.5564              0.8472
      {txt}rosen_pre_std {c |}    {res}0.4689             0.4515              0.8472
       {txt}cpcs_pre_std {c |}    {res}0.0105             0.0250              0.0629
           {txt}hhmember {c |}    {res}0.1345             0.1279              0.4206
          {txt}hhheadage {c |}    {res}0.9229             0.9251              0.9251
      {txt}hhheadeduyear {c |}    {res}0.0494             0.0500              0.2238
{hline 78}
{txt}
{com}. rwolf q2a q2c q2h tutor study_other followup_cog_std RSES_std CPCS_std, indepvar(treatment) reps(1000)
Bootstrap replications (1000). This may take some time.
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Romano-Wolf step-down adjusted p-values


Independent variable:  treatment
Outcome variables:   q2a q2c q2h tutor study_other followup_cog_std RSES_std CPCS_std
Number of resamples: 1000


{hline 78}
   Outcome Variable | Model p-value    Resample p-value    Romano-Wolf p-value
{hline 20}+{hline 57}
                {txt}q2a {c |}    {res}0.7471             0.7383              0.7862
                {txt}q2c {c |}    {res}0.4722             0.4406              0.7862
                {txt}q2h {c |}    {res}0.2801             0.2957              0.6174
              {txt}tutor {c |}    {res}0.0573             0.0480              0.2328
        {txt}study_other {c |}    {res}0.6083             0.5774              0.7862
   {txt}followup_cog_std {c |}    {res}0.0809             0.0669              0.2637
           {txt}RSES_std {c |}    {res}0.0030             0.0020              0.0180
           {txt}CPCS_std {c |}    {res}0.0011             0.0020              0.0110
{hline 78}
{txt}
{com}. 
. 
. /// Table
> tempname hh2
{txt}
{com}. file open `hh2' using "$path_output/summary_stat.tex", write replace
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "% Author: Kazuma Takakura" _newline
{txt}
{com}. file write `hh2' "% Date: `c(current_date)'" _newline
{txt}
{com}. file write `hh2' "% Time: `c(current_time)'" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. 
. file write `hh2' "\begin{c -(}table{c )-}[h!]\footnotesize" _newline
{txt}
{com}. file write `hh2' "  \centering" _newline
{txt}
{com}. file write `hh2' "  \caption{c -(}Summary Statistics{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:sumstat{c )-}" _newline
{txt}
{com}. file write `hh2' "\scalebox{c -(}1{c )-}{c -(}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}threeparttable{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "\begin{c -(}tabular{c )-}{c -(}lccccc{c )-}\toprule" _newline
{txt}
{com}. 
.   
. file write `hh2' " Dependent Variable & Treatment &  Control  & Difference & N   \\\midrule\midrule" _newline
{txt}
{com}. file write `hh2' " Panel A: Baseline & & & &   \\ " _newline
{txt}
{com}. file write `hh2' " DT score^{c -(}a{c )-} & " %04.3f (mean_tr_bl[1,1]) " & " %04.3f (mean_ct_bl[1,1]) " & " %04.3f (r2_DT_score_pre_std_mean[1,1]) `star_DT_score_pre_std' " & " (n_bl[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_bl[1,1]) " ] & [ " %04.3f (sd_ct_bl[1,1]) " ] & ( " %04.3f (r2_DT_score_pre_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.831) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. 
. file write `hh2' " RSES score^{c -(}a{c )-} & " %04.3f (mean_tr_bl[1,2]) " & " %04.3f (mean_ct_bl[1,2]) " & " %04.3f (r2_rosen_pre_std_mean[1,1]) `star_rosen_pre_std' " & "  (n_bl[1,2]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_bl[1,2]) " ] & [ " %04.3f (sd_ct_bl[1,2]) " ] & ( " %04.3f (r2_rosen_pre_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.831) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " CPCS score^{c -(}a{c )-} & " %04.3f (mean_tr_bl[1,3]) " & " %04.3f (mean_ct_bl[1,3]) " & " %04.3f (r2_cpcs_pre_std_mean[1,1]) `star_cpcs_pre_std' " & "  (n_bl[1,3]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_bl[1,3]) " ] & [ " %04.3f (sd_ct_bl[1,3]) " ] & ( " %04.3f (r2_cpcs_pre_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.059) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Household size & " %04.3f (mean_tr_parent[1,1]) " & " %04.3f (mean_ct_parent[1,1]) " & " %04.3f (r2_hhmember_mean[1,1]) `star_hhmember'  " & " (n_parent[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_parent[1,1]) " ] & [ " %04.3f (sd_ct_parent[1,1]) " ] & ( " %04.3f (r2_hhmember_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.464) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Household head age & " %04.3f (mean_tr_parent[1,2]) " & " %04.3f (mean_ct_parent[1,2]) " & " %04.3f (r2_hhheadage_mean[1,1]) `star_hhheadage' " & "  (n_parent[1,2]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_parent[1,2]) " ] & [ " %04.3f (sd_ct_parent[1,2]) " ] & ( " %04.3f (r2_hhheadage_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.920) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Household head education & " %04.3f (mean_tr_parent[1,3]) " & " %04.3f (mean_ct_parent[1,3]) " & " %04.3f (r2_hhheadeduyear_mean[1,1]) `star_hhheadeduyear' " & "  (n_parent[1,3]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_parent[1,3]) " ] & [ " %04.3f (sd_ct_parent[1,3]) " ] & ( " %04.3f (r2_hhheadeduyear_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.248) " \{c )-} &   \\ " _newline
{txt}
{com}. file write `hh2' " \\ "_newline
{txt}
{com}. 
. file write `hh2' " Panel B: Follow-up & & & &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " School attendance & " %04.3f (mean_tr_school[1,1]) " & " %04.3f (mean_ct_school[1,1]) " & " %04.3f (r2_q2a_mean[1,1]) `star_q2a' " & " (n_school[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_school[1,1]) " ] & [ " %04.3f (sd_ct_school[1,1]) " ] & ( " %04.3f (r2_q2a_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.787) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Grade repeat & " %04.3f (mean_tr_school[1,3]) " & " %04.3f (mean_ct_school[1,3]) " & " %04.3f (r2_q2c_mean[1,1]) `star_q2c' " & "  (n_school[1,3]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_school[1,3]) " ] & [ " %04.3f (sd_ct_school[1,3]) " ] & ( " %04.3f (r2_q2c_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.787) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Drop out & " %04.3f (mean_tr_school[1,4]) " & " %04.3f (mean_ct_school[1,4]) " & " %04.3f (r2_q2h_mean[1,1]) `star_q2h'  " & "  (n_school[1,4]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_school[1,4]) " ] & [ " %04.3f (sd_ct_school[1,4]) " ] & ( " %04.3f (r2_q2h_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.576) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Tutoring & " %04.3f (mean_tr_study[1,1]) " & " %04.3f (mean_ct_study[1,1]) " & " %04.3f (r2_tutor_mean[1,1]) `star_tutor'  " & " (n_study[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_study[1,1]) " ] & [ " %04.3f (sd_ct_study[1,1]) " ] & ( " %04.3f (r2_tutor_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.230) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Self-study & " %04.3f (mean_tr_study[1,2]) " & " %04.3f (mean_ct_study[1,2]) " & " %04.3f (r2_study_other_mean[1,1]) `star_study_other' " & "  (n_study[1,2]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_study[1,2]) " ] & [ " %04.3f (sd_ct_study[1,2]) " ] & ( " %04.3f (r2_study_other_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.787) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " Rapid math test score^{c -(}a{c )-} & " %04.3f (mean_tr_cog[1,1]) " & " %04.3f (mean_ct_cog[1,1]) " & " %04.3f (r2_followup_cog_std_mean[1,1]) `star_followup_cog_std'  "  & "  (n_cog[1,1]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_cog[1,1]) " ] & [ " %04.3f (sd_ct_cog[1,1]) " ] & ( " %04.3f (r2_followup_cog_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.270) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " RSES score^{c -(}a{c )-} & " %04.3f (mean_tr_noncog[1,2]) " & " %04.3f (mean_ct_noncog[1,2]) " & " %04.3f (r2_RSES_std_mean[1,1])   `star_RSES_std' " & " (n_noncog[1,2]) "  \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_noncog[1,2]) " ] & [ " %04.3f (sd_ct_noncog[1,2]) " ] & ( " %04.3f (r2_RSES_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.011) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. file write `hh2' " CPCS score^{c -(}a{c )-} & " %04.3f (mean_tr_noncog[1,3]) " & " %04.3f (mean_ct_noncog[1,3]) " & " %04.3f (r2_CPCS_std_mean[1,1])   `star_CPCS_std' "&  " (n_noncog[1,3]) " \\ " _newline
{txt}
{com}. file write `hh2' "                               & [ " %04.3f (sd_tr_noncog[1,3]) " ] & [ " %04.3f (sd_ct_noncog[1,3]) " ] & ( " %04.3f (r2_CPCS_std_pv[1,1]) " ) &   \\ " _newline
{txt}
{com}. file write `hh2' "                               &   &   & \{c -(} " %04.3f (0.006) " \{c )-} &   \\ " _newline
{txt}
{com}. 
. 
. file write `hh2' "\midrule" _newline
{txt}
{com}. file write `hh2' "\end{c -(}tabular{c )-}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\item (a) Variables are standardized using the average and variance of the whole follow-up sample in the February 2022 survey. " _newline
{txt}
{com}. file write `hh2' "\item (b) Standard deviations are reported in square brackets.  " _newline
{txt}
{com}. file write `hh2' "\item (c) Wild clustered bootstrap p-values are reported within parentheses. Clusters are schools at the baseline. There are 34 clusters. " _newline
{txt}
{com}. file write `hh2' "\item (d) Romano-Wolf multiple hypothesis testing p-values are reported in curly brackets. This test is conducted separately for the baseline variables and the follow-up variables." _newline
{txt}
{com}. file write `hh2' "\item (e) Statistical significance is indicated by stars based on the wild clustered bootstrap p-values reported in parentheses: $*$ denotes significance at the 10\% level, $∗∗$ at the 5\% level, and $∗∗∗$ at the 1\% level.  " _newline
{txt}
{com}. file write `hh2' "\end{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}threeparttable{c )-}" _newline
{txt}
{com}. file write `hh2' "{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}table{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. file close `hh2'
{txt}
{com}. 
{txt}end of do-file

{com}. do "/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/do_files/2_table_4.do"
{txt}
{com}. * This is the do file to create "Table 4. Heterogeneity among Baseline Abilites"
. set seed 123
{txt}
{com}. 
. use "$path_data/temp/followup_student_parents_matched", replace
{txt}
{com}. 
. egen DT_score_pre_std_med = median(DT_score_pre_std)
{txt}
{com}. gen DT_score_pre_std_upper50 = 1 if DT_score_pre_std>DT_score_pre_std_med
{txt}(121 missing values generated)

{com}. recode DT_score_pre_std_upper50 (.=0)
{txt}(121 changes made to {bf:DT_score_pre_std_upper50})

{com}. 
. egen rosen_pre_std_med = median(rosen_pre_std)
{txt}
{com}. gen rosen_pre_std_upper50 = 1 if rosen_pre_std>rosen_pre_std_med
{txt}(128 missing values generated)

{com}. recode rosen_pre_std_upper50 (.=0)
{txt}(128 changes made to {bf:rosen_pre_std_upper50})

{com}. rename rosen_pre_std_upper50 RSES_std_upper50
{res}{txt}
{com}. 
. egen cpcs_pre_std_med = median(cpcs_pre_std)
{txt}
{com}. gen cpcs_pre_std_upper50 = 1 if cpcs_pre_std>cpcs_pre_std_med
{txt}(135 missing values generated)

{com}. recode cpcs_pre_std_upper50 (.=0)
{txt}(135 changes made to {bf:cpcs_pre_std_upper50})

{com}. rename cpcs_pre_std_upper50 CPCS_std_upper50
{res}{txt}
{com}. 
. 
. 
. /// Cognitive
> wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 1, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}122
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 28
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}4.4
{col 69}{txt}max{col 72} = {res} 11
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2}-.2944524{col 38}{space 1}  -1.31{col 46}{space 3}0.218{col 54}{space 3}-.7968218{col 66}{space 3} .2159552
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_u = e(N)
{txt}
{com}. scalar n_clust_cog_u = e(N_clust)
{txt}
{com}. matrix r2_followup_cog_std_temp = r(table)
{txt}
{com}. 
. 
. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix r2_followup_cog_std_upper_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix r2_followup_cog_std_upper_mean[1,`j'] = r2_followup_cog_std_temp[1,`j']
{txt}  3{com}. * standard error
. * matrix r2_followup_cog_std_upper_se[1,`j'] = r2_followup_cog_std_temp[2,`j']
. * p value
. matrix r2_followup_cog_std_upper_pv[1,`j'] = r2_followup_cog_std_temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. 
. wildbootstrap reg followup_cog_std treatment if DT_score_pre_std_upper50 == 0, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:26})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}121
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 30
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}4.0
{col 69}{txt}max{col 72} = {res} 10
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} -.156118{col 38}{space 1}  -0.67{col 46}{space 3}0.558{col 54}{space 3}-.6174928{col 66}{space 3} .4100967
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. scalar n_cog_l = e(N)
{txt}
{com}. scalar n_clust_cog_l = e(N_clust)
{txt}
{com}. matrix r2_followup_cog_std_temp = r(table)
{txt}
{com}. 
. 
. foreach s in mean se pv obs {c -(}
{txt}  2{com}.                 matrix r2_followup_cog_std_lower_`s' = J(1,2,.)
{txt}  3{com}. {c )-}
{txt}
{com}. 
. foreach j in 1 2 {c -(}
{txt}  2{com}. * beta
. matrix r2_followup_cog_std_lower_mean[1,`j'] = r2_followup_cog_std_temp[1,`j']
{txt}  3{com}. * standard error
. * matrix r2_followup_cog_std_lower_se[1,`j'] = r2_followup_cog_std_temp[2,`j']
. * p value
. matrix r2_followup_cog_std_lower_pv[1,`j'] = r2_followup_cog_std_temp[3,`j']
{txt}  4{com}. {c )-}
{txt}
{com}. 
. wildbootstrap reg followup_cog_std i.treatment##i.DT_score_pre_std_upper50, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:1.treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:1.treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:1.DT_score_pre_std_upper50 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:1.DT_score_pre_std_upper50}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:1.treatment#1.DT_score_pre_std_upper50 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:1.treatment#1.DT_score_pre_std_upper50}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}243
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.4
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}        followup_cog_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraints             {col 26}{c |}
{res}{col 1}{text}         1.treatment = 0{col 26}{c |}{result}{space 2} -.156118{col 38}{space 1}  -0.67{col 46}{space 3}0.536{col 54}{space 3}-.6346894{col 66}{space 3} .4253064
{col 1}{text}1.DT_score_pre_std_upper{col 26}{c |}
{res}{col 1}{text}                  50 = 0{col 26}{c |}{result}{space 2} .1420924{col 38}{space 1}   0.67{col 46}{space 3}0.512{col 54}{space 3} -.380663{col 66}{space 3} .6680267
{col 26}{text}{c |}
{res}{col 1}{text}            1.treatment#{col 26}{c |}
{res}{col 1}{text}1.DT_score_pre_std_upper{col 26}{c |}
{res}{col 1}{text}                  50 = 0{col 26}{c |}{result}{space 2}-.1383344{col 38}{space 1}  -0.45{col 46}{space 3}0.680{col 54}{space 3}-.8103858{col 66}{space 3} .4858404
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix cog_difference = r(table)
{txt}
{com}. 
. 
.     
. /// Non cognitive
> 
. 
. foreach dep in RSES_std CPCS_std{c -(}
{txt}  2{com}.     
.     wildbootstrap reg `dep' treatment if `dep'_upper50 == 1, cluster(school_no) reps(1000)
{txt}  3{com}.         scalar n_`dep'_u = e(N)
{txt}  4{com}.         scalar n_clust_`dep'_u = e(N_clust)
{txt}  5{com}.         matrix r2_`dep'_temp = r(table)
{txt}  6{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt}  7{com}.                 matrix r2_`dep'_upper_`s' = J(1,2,.)
{txt}  8{com}.         {c )-}
{txt}  9{com}. 
.         foreach j in 1 2 {c -(}
{txt} 10{com}.         * beta
.         matrix r2_`dep'_upper_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt} 11{com}.         * standard error
.         * matrix r2_`dep'_upper_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_upper_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 12{com}.         {c )-}
{txt} 13{com}. 
. 
.         wildbootstrap reg `dep' treatment if `dep'_upper50 == 0, cluster(school_no) reps(1000)
{txt} 14{com}.         scalar n_`dep'_l = e(N)
{txt} 15{com}.         scalar n_clust_`dep'_l = e(N_clust)
{txt} 16{com}.         matrix r2_`dep'_temp = r(table)
{txt} 17{com}. 
. 
.     foreach s in mean se pv obs {c -(}
{txt} 18{com}.                 matrix r2_`dep'_lower_`s' = J(1,2,.)
{txt} 19{com}.         {c )-}
{txt} 20{com}. 
.         foreach j in 1 2 {c -(}
{txt} 21{com}.         * beta
.         matrix r2_`dep'_lower_mean[1,`j'] = r2_`dep'_temp[1,`j']
{txt} 22{com}.         * standard error
.         * matrix r2_`dep'_lower_se[1,`j'] = r2_`dep'_temp[2,`j']
.         * p value
.         matrix r2_`dep'_lower_pv[1,`j'] = r2_`dep'_temp[3,`j']
{txt} 23{com}.         {c )-}
{txt} 24{com}. 
. {c )-}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}112
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 28
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}4.0
{col 69}{txt}max{col 72} = {res} 11
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .7750596{col 38}{space 1}   3.24{col 46}{space 3}0.008{col 54}{space 3} .2893885{col 66}{space 3} 1.282941
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}124
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 29
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}4.3
{col 69}{txt}max{col 72} = {res} 10
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .0948848{col 38}{space 1}   0.43{col 46}{space 3}0.674{col 54}{space 3} -.373688{col 66}{space 3} .5897037
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:19})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}105
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 30
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}3.5
{col 69}{txt}max{col 72} = {res} 10
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .6274947{col 38}{space 1}   2.84{col 46}{space 3}0.014{col 54}{space 3} .1867966{col 66}{space 3} 1.064869
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:25})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}131
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 28
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  2
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}4.7
{col 69}{txt}max{col 72} = {res} 10
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraint              {col 26}{c |}
{res}{col 1}{text}           treatment = 0{col 26}{c |}{result}{space 2} .3378141{col 38}{space 1}   1.31{col 46}{space 3}0.182{col 54}{space 3}-.1932436{col 66}{space 3}  .895076
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. 
. wildbootstrap reg RSES_std i.treatment##i.RSES_std_upper50, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:1.treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:1.treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:1.RSES_std_upper50 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:1.RSES_std_upper50}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text} done{text} ({result:22})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:1.treatment#1.RSES_std_upper50 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:1.treatment#1.RSES_std_upper50}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}236
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.2
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                RSES_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraints             {col 26}{c |}
{res}{col 1}{text}         1.treatment = 0{col 26}{c |}{result}{space 2} .0948848{col 38}{space 1}   0.43{col 46}{space 3}0.710{col 54}{space 3}-.4216247{col 66}{space 3} .5614497
{col 1}{text}  1.RSES_std_upper50 = 0{col 26}{c |}{result}{space 2}-.5067505{col 38}{space 1}  -3.63{col 46}{space 3}0.002{col 54}{space 3}-.7931992{col 66}{space 3}-.2202677
{col 1}{text}            1.treatment#{col 26}{c |}
{res}{col 1}{text}  1.RSES_std_upper50 = 0{col 26}{c |}{result}{space 2} .6801748{col 38}{space 1}   2.84{col 46}{space 3}0.008{col 54}{space 3} .1765929{col 66}{space 3} 1.179148
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix RSES_difference = r(table)
{txt}
{com}. 
. wildbootstrap reg CPCS_std i.treatment##i.CPCS_std_upper50, cluster(school_no) reps(1000)
{res}
{txt}{p}Performing {res}    1,000{txt} replications for p-value for {bf:1.treatment = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:1.treatment}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text} done{text} ({result:23})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:1.CPCS_std_upper50 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:1.CPCS_std_upper50}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text} done{text} ({result:21})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text} done{text} ({result:27})

{p}Performing {res}    1,000{txt} replications for p-value for {bf:1.treatment#1.CPCS_std_upper50 = 0} {txt}...{p_end}
{res}{txt}Computing confidence interval for {bf:1.treatment#1.CPCS_std_upper50}
{text}  Lower bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text}.{text}.{text}.{text}.{text} done{text} ({result:24})
{text}  Upper bound{text}: {text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}10{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}.{text}20{text} done{text} ({result:20})
{res}
{txt}Wild cluster bootstrap{col 54}{txt}Number of obs{col 72} = {res}236
{txt}Linear regression{col 54}{txt}Number of clusters{col 72} = {res} 33
{col 54}{txt}Cluster size:
{txt}Cluster variable: {res:school_no}{col 69}{txt}min{col 72} = {res}  1
{txt}Error weight: Rademacher{col 69}{txt}avg{col 72} = {res}7.2
{col 69}{txt}max{col 72} = {res} 13
{col 1}{text}{hline 25}{c TT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}                CPCS_std{col 26}{c |}   Estimate{col 38}      t{col 46} p-value{col 54}    [95% conf. interval]
{res}{col 1}{text}{hline 25}{c +}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{col 1}{text}constraints             {col 26}{c |}
{res}{col 1}{text}         1.treatment = 0{col 26}{c |}{result}{space 2} .3378141{col 38}{space 1}   1.31{col 46}{space 3}0.226{col 54}{space 3}-.2401429{col 66}{space 3} .8560969
{col 1}{text}  1.CPCS_std_upper50 = 0{col 26}{c |}{result}{space 2}-.2857744{col 38}{space 1}  -1.34{col 46}{space 3}0.210{col 54}{space 3}-.7190431{col 66}{space 3} .1735032
{col 1}{text}            1.treatment#{col 26}{c |}
{res}{col 1}{text}  1.CPCS_std_upper50 = 0{col 26}{c |}{result}{space 2} .2896806{col 38}{space 1}   1.01{col 46}{space 3}0.324{col 54}{space 3}-.3368099{col 66}{space 3} .8833211
{col 1}{text}{hline 25}{c BT}{hline 11}{hline 8}{hline 8}{hline 12}{hline 12}
{res}{txt}
{com}. matrix CPCS_difference = r(table)
{txt}
{com}. 
. 
. // significant level
. 
. local outcome followup_cog_std RSES_std CPCS_std
{txt}
{com}. local hetero upper lower
{txt}
{com}. 
. foreach dep in `outcome'{c -(}
{txt}  2{com}. foreach h in `hetero'{c -(}
{txt}  3{com}.                 if r2_`dep'_`h'_pv[1,1]<=0.01 {c -(}
{txt}  4{com}.                         local star_`dep'_`h' %3s "***"
{txt}  5{com}.                 {c )-}
{txt}  6{com}.                 else if (r2_`dep'_`h'_pv[1,1]>0.01) & (r2_`dep'_`h'_pv[1,1]<=0.05) {c -(}
{txt}  7{com}.                         local star_`dep'_`h' %2s "**"
{txt}  8{com}.                 {c )-}
{txt}  9{com}.                 else if (r2_`dep'_`h'_pv[1,1]>0.05) & (r2_`dep'_`h'_pv[1,1]<=0.10) {c -(}
{txt} 10{com}.                         local star_`dep'_`h' %1s "*"
{txt} 11{com}.                 {c )-}
{txt} 12{com}.                 else {c -(}
{txt} 13{com}.                         local star_`dep'_`h'  ""
{txt} 14{com}.                 {c )-}
{txt} 15{com}. {c )-} 
{txt} 16{com}. {c )-}
{txt}
{com}. 
. /// Table
> tempname hh2
{txt}
{com}. file open `hh2' using "$path_output/hetero.tex", write replace
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "% Author: Kazuma Takakura" _newline
{txt}
{com}. file write `hh2' "% Date: `c(current_date)'" _newline
{txt}
{com}. file write `hh2' "% Time: `c(current_time)'" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. 
. file write `hh2' "\begin{c -(}table{c )-}[h!]\footnotesize" _newline
{txt}
{com}. file write `hh2' "  \centering" _newline
{txt}
{com}. file write `hh2' "  \caption{c -(}Heterogeneity among Baseline Abilites{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:hetero{c )-}" _newline
{txt}
{com}. file write `hh2' "\scalebox{c -(}1{c )-}{c -(}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}threeparttable{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "\begin{c -(}tabular{c )-}{c -(}lccc{c )-}\toprule" _newline
{txt}
{com}. 
. file write `hh2' "  & Top 50\%^{c -(}b{c )-}  & Bottom 50\%^{c -(}b{c )-} & Differences  \\\midrule" _newline
{txt}
{com}. file write `hh2' "\multicolumn{c -(}4{c )-}{c -(}c{c )-}{c -(}Panel A: Rapid math test score^{c -(}a{c )-}{c )-}\\\midrule" _newline
{txt}
{com}. file write `hh2' " Treatment & " %04.3f (r2_followup_cog_std_upper_mean[1,1]) `star_followup_cog_std_upper' "  & " %04.3f (r2_followup_cog_std_lower_mean[1,1]) `star_followup_cog_std_lower' " &  " %04.3f (cog_difference[1,3]) " \\" _newline
{txt}
{com}. file write `hh2' " & ( " %04.3f (r2_followup_cog_std_upper_pv[1,1]) " ) & ( " %04.3f (r2_followup_cog_std_lower_pv[1,1]) " ) & ( " %04.3f (cog_difference[3,3]) " ) \\ " _newline
{txt}
{com}. file write `hh2' " Observation &  " %02.0f ( n_cog_u ) " & " %02.0f ( n_cog_l ) " &  \\ " _newline
{txt}
{com}. file write `hh2' " N of clusters &  " %02.0f ( n_clust_cog_u ) " & " %02.0f ( n_clust_cog_l ) " &  \\\midrule " _newline
{txt}
{com}. 
. file write `hh2' "\multicolumn{c -(}4{c )-}{c -(}c{c )-}{c -(}Panel B: RSES score^{c -(}a{c )-}{c )-}\\\midrule" _newline
{txt}
{com}. file write `hh2' " Treatment & " %04.3f (r2_RSES_std_upper_mean[1,1]) `star_RSES_std_upper' "  & " %04.3f (r2_RSES_std_lower_mean[1,1]) `star_RSES_std_lower' " &  " %04.3f (RSES_difference[1,3]) " *** \\" _newline
{txt}
{com}. file write `hh2' " & ( " %04.3f (r2_RSES_std_upper_pv[1,1]) " ) & ( " %04.3f (r2_RSES_std_lower_pv[1,1]) " ) & ( " %04.3f (RSES_difference[3,3]) " ) \\ " _newline
{txt}
{com}. file write `hh2' " Observation &  " %02.0f ( n_RSES_std_u ) " & " %02.0f ( n_RSES_std_l ) " &  \\ " _newline
{txt}
{com}. file write `hh2' " N of clusters &  " %02.0f ( n_clust_RSES_std_u ) " & " %02.0f ( n_clust_RSES_std_l ) " &  \\\midrule " _newline
{txt}
{com}. 
. file write `hh2' "\multicolumn{c -(}4{c )-}{c -(}c{c )-}{c -(}Panel C: CPCS score^{c -(}a{c )-}{c )-}\\\midrule" _newline
{txt}
{com}. file write `hh2' " Treatment & " %04.3f (r2_CPCS_std_upper_mean[1,1]) `star_CPCS_std_upper' "  & " %04.3f (r2_CPCS_std_lower_mean[1,1]) `star_CPCS_std_lower' " &  " %04.3f (CPCS_difference[1,3]) " \\" _newline
{txt}
{com}. file write `hh2' " & ( " %04.3f (r2_CPCS_std_upper_pv[1,1]) " ) & ( " %04.3f (r2_CPCS_std_lower_pv[1,1]) " ) & ( " %04.3f (CPCS_difference[3,3]) " ) \\ " _newline
{txt}
{com}. file write `hh2' " Observation &  " %02.0f ( n_CPCS_std_u ) " & " %02.0f ( n_CPCS_std_l ) " &  \\ " _newline
{txt}
{com}. file write `hh2' " N of clusters &  " %02.0f ( n_clust_CPCS_std_u ) " & " %02.0f ( n_clust_CPCS_std_l ) " &  \\\midrule " _newline
{txt}
{com}. 
. file write `hh2' "\end{c -(}tabular{c )-}" _newline
{txt}
{com}. file write `hh2' "\begin{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\item (a) Dependent variables are standardized using the average and variance of the whole follow-up sample in the February 2022 survey." _newline
{txt}
{com}. file write `hh2' "\item (b) Cutoffs are created based on whether their ability to perform each item at the baseline was higher or lower than the median. " _newline
{txt}
{com}. file write `hh2' "\item (c) Wild cluster bootstrap p-values are reported within parentheses. Clusters are schools at the baseline. " _newline
{txt}
{com}. file write `hh2' "\item (d) $^*$ Significant at 10\% level; $^{c -(}**{c )-}$ significant at 5\% level; $^{c -(}***{c )-}$ significant at 1\% level. " _newline
{txt}
{com}. file write `hh2' "\end{c -(}tablenotes{c )-}" _newline
{txt}
{com}. file write `hh2' "\end{c -(}threeparttable{c )-}" _newline
{txt}
{com}. file write `hh2' "{c )-}" _newline
{txt}
{com}. file write `hh2' "\label{c -(}tab:addlabel{c )-}%" _newline
{txt}
{com}. file write `hh2' "\end{c -(}table{c )-}" _newline
{txt}
{com}. 
. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. file write `hh2' "" _newline
{txt}
{com}. 
. file close `hh2'
{txt}
{com}. 
. 
{txt}end of do-file

{com}. do "/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/do_files/3_figure_B1.do"
{txt}
{com}. * This is the do file to create "Figure B1. Propensity Score Overlap"
. 
. use "$path_data/temp/followup_student_parents_matched", replace
{txt}
{com}. 
. // variable
. gen gend = q1d - 1
{txt}
{com}. 
. 
. local controls DT_score_pre_std_missing_0 rosen_pre_std_missing_0 cpcs_pre_std_missing_0 i.grade gend branch1 branch2 branch3 income_source1 income_source2 income_source3 income_source4 last_income_per_member hhmember hhheadage hhheadeduyear phone_survey age_tchr
{txt}
{com}. 
. 
. // PS 
. logit treatment `controls'

{res}{txt}Iteration 0:{space 2}Log likelihood = {res:-163.86073}  
Iteration 1:{space 2}Log likelihood = {res:-139.65506}  
Iteration 2:{space 2}Log likelihood = {res:-139.28345}  
Iteration 3:{space 2}Log likelihood = {res:-139.28041}  
Iteration 4:{space 2}Log likelihood = {res:-139.28041}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:243}
{txt}{col 57}{lalign 13:LR chi2({res:18})}{col 70} = {res}{ralign 6:49.16}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0001}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-139.28041}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.1500}

{txt}{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                 treatment{col 28}{c |} Coefficient{col 40}  Std. err.{col 52}      z{col 60}   P>|z|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
DT_score_pre_std_missing_0 {c |}{col 28}{res}{space 2}-.2108634{col 40}{space 2} .1559605{col 51}{space 1}   -1.35{col 60}{space 3}0.176{col 68}{space 4}-.5165404{col 81}{space 3} .0948136
{txt}{space 3}rosen_pre_std_missing_0 {c |}{col 28}{res}{space 2}-1.156243{col 40}{space 2} .3670308{col 51}{space 1}   -3.15{col 60}{space 3}0.002{col 68}{space 4} -1.87561{col 81}{space 3} -.436876
{txt}{space 4}cpcs_pre_std_missing_0 {c |}{col 28}{res}{space 2} 1.363271{col 40}{space 2}  .373272{col 51}{space 1}    3.65{col 60}{space 3}0.000{col 68}{space 4} .6316711{col 81}{space 3} 2.094871
{txt}{space 19}4.grade {c |}{col 28}{res}{space 2} .2601036{col 40}{space 2} .3425485{col 51}{space 1}    0.76{col 60}{space 3}0.448{col 68}{space 4}-.4112792{col 81}{space 3} .9314864
{txt}{space 22}gend {c |}{col 28}{res}{space 2} .0431333{col 40}{space 2} .3136935{col 51}{space 1}    0.14{col 60}{space 3}0.891{col 68}{space 4}-.5716947{col 81}{space 3} .6579613
{txt}{space 19}branch1 {c |}{col 28}{res}{space 2}-.8730908{col 40}{space 2} .5101577{col 51}{space 1}   -1.71{col 60}{space 3}0.087{col 68}{space 4}-1.872982{col 81}{space 3} .1267998
{txt}{space 19}branch2 {c |}{col 28}{res}{space 2} .3672116{col 40}{space 2} .5857284{col 51}{space 1}    0.63{col 60}{space 3}0.531{col 68}{space 4} -.780795{col 81}{space 3} 1.515218
{txt}{space 19}branch3 {c |}{col 28}{res}{space 2}-.9819822{col 40}{space 2} .4213023{col 51}{space 1}   -2.33{col 60}{space 3}0.020{col 68}{space 4} -1.80772{col 81}{space 3}-.1562449
{txt}{space 12}income_source1 {c |}{col 28}{res}{space 2}-1.392694{col 40}{space 2} 1.198015{col 51}{space 1}   -1.16{col 60}{space 3}0.245{col 68}{space 4} -3.74076{col 81}{space 3} .9553711
{txt}{space 12}income_source2 {c |}{col 28}{res}{space 2}-.1140878{col 40}{space 2} .9307761{col 51}{space 1}   -0.12{col 60}{space 3}0.902{col 68}{space 4}-1.938375{col 81}{space 3}   1.7102
{txt}{space 12}income_source3 {c |}{col 28}{res}{space 2}-.4115209{col 40}{space 2} .8999542{col 51}{space 1}   -0.46{col 60}{space 3}0.647{col 68}{space 4}-2.175399{col 81}{space 3} 1.352357
{txt}{space 12}income_source4 {c |}{col 28}{res}{space 2} 1.910332{col 40}{space 2} 2.758545{col 51}{space 1}    0.69{col 60}{space 3}0.489{col 68}{space 4}-3.496317{col 81}{space 3} 7.316982
{txt}{space 4}last_income_per_member {c |}{col 28}{res}{space 2}-.0001351{col 40}{space 2} .0001481{col 51}{space 1}   -0.91{col 60}{space 3}0.362{col 68}{space 4}-.0004253{col 81}{space 3} .0001551
{txt}{space 18}hhmember {c |}{col 28}{res}{space 2} .1845254{col 40}{space 2} .1265051{col 51}{space 1}    1.46{col 60}{space 3}0.145{col 68}{space 4}  -.06342{col 81}{space 3} .4324708
{txt}{space 17}hhheadage {c |}{col 28}{res}{space 2}-.0072255{col 40}{space 2} .0169493{col 51}{space 1}   -0.43{col 60}{space 3}0.670{col 68}{space 4}-.0404454{col 81}{space 3} .0259945
{txt}{space 13}hhheadeduyear {c |}{col 28}{res}{space 2}-.0778674{col 40}{space 2} .0471652{col 51}{space 1}   -1.65{col 60}{space 3}0.099{col 68}{space 4}-.1703095{col 81}{space 3} .0145748
{txt}{space 14}phone_survey {c |}{col 28}{res}{space 2}-.0776531{col 40}{space 2} .3471772{col 51}{space 1}   -0.22{col 60}{space 3}0.823{col 68}{space 4}-.7581078{col 81}{space 3} .6028016
{txt}{space 18}age_tchr {c |}{col 28}{res}{space 2}-.0365818{col 40}{space 2} .0244024{col 51}{space 1}   -1.50{col 60}{space 3}0.134{col 68}{space 4}-.0844097{col 81}{space 3}  .011246
{txt}{space 21}_cons {c |}{col 28}{res}{space 2}  2.00163{col 40}{space 2} 1.433552{col 51}{space 1}    1.40{col 60}{space 3}0.163{col 68}{space 4}-.8080801{col 81}{space 3} 4.811341
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. predict ps
{txt}(option {bf:pr} assumed; Pr(treatment))

{com}. label define treatlabel 1 "treatment" 0 "control"
{txt}
{com}. label val treatment treatlabel
{txt}
{com}. * hist ps, by(treatment) bin(30)
. * graph save "`pardir'/fig_ps.png", replace
. 
. ksmirnov ps, by(treatment)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
control            {res}  0.3634       0.000
{txt}treatment          {res}  0.0000       1.000
{txt}Combined K-S       {res}  0.3634       0.000
{txt}
{com}. 
. twoway (kdensity ps if treatment == 0, bwidth(0.01) lcolor(blue)) (kdensity ps if treatment == 1, bwidth(0.01) lcolor(red)), ///
>        legend(label(1 "Control") label(2 "Treatment")) ///
>            ytitle("density") xtitle("propensity score")
{res}{txt}
{com}. graph save "$path_output/fig_ps.png", replace
{res}{txt}file {bf:/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/outputs/fig_ps.png} saved as .gph format

{com}. 
{txt}end of do-file

{com}. graph export "/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/outputs/fig_ps.png", as(png) name("Graph") replace
{txt}{p 0 4 2}
file {bf}
/Users/takakurakazuma/Dropbox/Kumon BRAC Bangladesh project/BRAC School Project/Field Experiment/Follow-up/Submission to a journal/EER_special_issue/replication_package/outputs/fig_ps.png{rm}
saved as
PNG
format
{p_end}

{com}. 